research topics in computer education

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How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2023-2024

• Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
• Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
• Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
• Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

• Machine learning driven design
• Generative models for structured discrete objects
• Approximate inference in probabilistic models
• Accelerating solutions to partial differential equations
• Innovative uses of automatic differentiation
• Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

• Research Areas: Formal methods, programming languages, compilers, computer security.
• Software verification (for which taking COS 326 / COS 510 is helpful preparation)
• Game theory of poker or other games (for which COS 217 / 226 are helpful)
• Computer game-playing programs (for which COS 217 / 226)
•  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

• Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
• Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
• Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
• Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
• Any topic in theoretical computer science.

David August, Room 221

• Research Areas: Computer Architecture, Compilers, Parallelism
• Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
• Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
• Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

Available for Spring 2024 single-semester IW, only

• Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
• Topics in computational and communication complexity.
• Applications of information theory in complexity theory.
• Algorithms for problems under real-life assumptions.
• Game theory, network effects
• Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

• Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
• Methods for collaborative and continual learning.
• Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

• Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
• Natural algorithms (flocking, swarming, social networks, etc).
• Sublinear algorithms
• Self-improving algorithms
• Markov data structures

Danqi Chen, Room 412

Not available for IW or thesis advising, 2023-2024

• My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
• Representation learning for text and knowledge bases
• Pre-training and transfer learning
• Question answering and reading comprehension
• Information extraction
• Text summarization
• Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

Available for single-semester and senior thesis advising, 2023-2024

• Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)

Jia Deng, Room 423

Available for Fall 2023 single-semester IW, only

•  Research Areas: Computer Vision, Machine Learning.
• Object recognition and action recognition
• Deep Learning, autoML, meta-learning
• Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

• Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

• Research Areas:  Software engineering; software engineering education.
• Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
• In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

Not available for IW or thesis advising, 2023-2024.

• Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
• Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

• Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
• Using RL algorithms to applications in science and engineering.
• Emergent behavior of RL algorithms on high-fidelity robotic simulators.
• Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

No longer available for single-term IW and senior thesis advising, 2023-2024

• Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
• Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
• Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
• Quantitative approaches to theoretical linguistics questions
• Extensions and interfaces for WordNet (English and WN in other languages),
• Applications of WordNet(s), including:
• Foreign language tutoring systems,
• Spelling correction software,
• Word-finding/suggestion software for ordinary users and people with memory problems,
• Machine Translation 
• Sentiment and Opinion detection
• Automatic reasoning and inferencing
• Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

No longer available for single-semester IW and senior thesis advising, 2023-2024

• Networking and telecommunications
• Learning, perception, and intelligence, artificial and otherwise;
• Human-computer interaction and computer-supported cooperative work
• Online education, especially in Computer Science Education
• Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
• Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

• Research Areas: Distributed systems, security, networking
• Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

• Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
• Develop a technique for understanding AI models
• Design a AI model that is interpretable by design
• Build a paradigm for detecting and/or correcting failure points in an AI model
• Analyze an existing AI model and/or dataset to better understand its failure points
• Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
• Develop a software package for explainable AI
• Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

• Research Areas: Formal methods, program analysis, logic decision procedures
• Finding bugs in open source software using automatic verification tools
• Software verification (program analysis, model checking, test generation)
• Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

• Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
• Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
• Implementation and algorithm engineering for control, reinforcement learning and robotics
• Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

• Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
• Optical Neural Networks
• Hardware-in-the-loop Holography
• Zero-shot and Simulation-only Learning
• Object recognition in extreme conditions
• 3D Scene Representations for View Generation and Inverse Problems
• Long-range Imaging in Scattering Media
• Hardware-in-the-loop Illumination and Sensor Optimization
• Inverse Lidar Design
• Phase Retrieval Algorithms
• Proximal Algorithms for Learning and Inference
• Domain-Specific Language for Optics Design

Kyle Jamieson, Room 306

• Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
• See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

• Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
• Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
• Software visualization tools for education
• Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

• Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
• Application-oriented languages, scripting languages.
• Tools; user interfaces
• Digital humanities

Zachary Kincaid, Room 219

• Research areas: programming languages, program analysis, program verification, automated reasoning
• Independent Research Topics:
• Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
• Design a domain-specific programming language, or prototype a new feature for an existing language.
• Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

Available for single-term IW and senior thesis advising, 2023-2024

• Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Amit Levy, Room 307

• Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
• Distributed hardware testing infrastructure
• Second factor security tokens
• Low-power wireless network protocol implementation
• USB device driver implementation

Kai Li, Room 321

• Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
• Fast communication mechanisms for heterogeneous clusters.
• Approximate nearest-neighbor search for high dimensional data.
• Data analysis and prediction of in-patient medical data.
• Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

• Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
• Explore new statistical retrieval models for document retrieval and question answering.
• Apply AI in various fields.
• Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
• Any interesting project related to AI, machine learning, and data analysis.

Wyatt Lloyd, Room 323

• Research areas: Distributed Systems
• Caching algorithms and implementations
• Storage systems
• Distributed transaction algorithms and implementations

Margaret Martonosi, Room 208

• Quantum Computing research, particularly related to architecture and compiler issues for QC.
• Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
• Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
• Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

• Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
• Assessing the effects of government policies, both in the public and private sectors.
• Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
• Developing new tools to improve government processes and offer policy alternatives.

Andrés Monroy-Hernández, Room 405

• Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
• Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
• Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

• Research areas: Distributed systems, high-throughput computing, computer science/engineering education
• Expansion, improvement, and evaluation of open-source distributed computing software.
• Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
• Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
• Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

• Research areas: Natural Language Processing, Reinforcement Learning
• Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
• Transfer learning/generalization in NLP
• Techniques for generating natural language
• Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

• Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
• Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
• Possible research topics include:

1.   Quantum algorithms and circuits:

• i. Design or simulation quantum circuits implementing quantum algorithms.
• ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

• i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
• ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
• iii. Necessary and sufficient conditions for tractability of Weighted problems.
• iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

• i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

• Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
• Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

• Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
• Implementation and application of algorithms to infer evolutionary processes in cancer
• Identifying correlations between combinations of genomic mutations in human and cancer genomes
• Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
• Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

• Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
• Constructing a new method to explain a model / create an interpretable by design model
• Analyzing a current model / dataset to understand bias within the model/dataset
• Proposing new fairness evaluations
• Proposing new methods to train to improve fairness
• Developing synthetic datasets for fairness / interpretability benchmarks
• Understanding robustness of models

Ran Raz, Room 240

• Research Area: Computational Complexity
• Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

• Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
• Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
• Investigate approaches to robust 3D scan matching
• Model and compensate for imperfections in 3D printing
• Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

• Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
• Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
• Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
• Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

• Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
• Gamification of neuroscience (EyeWire  2.0)
• Semantic segmentation and object detection in brain images from microscopy
• Computational analysis of brain structure and function
• Neural network theories of brain function

Jaswinder Pal Singh, Room 324

• Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
• Develop a startup company idea, and build a plan/prototype for it.
• Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
• Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
• Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
• Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

• Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
• Whole and cross-genome methods for predicting protein function and protein-protein interactions.
• Analysis and prediction of biological networks.
• Computational methods for inferring specific aspects of protein structure from protein sequence data.
• Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

Available for single-semester IW and senior thesis advising, 2022-2023

• Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
• Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
• Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

• Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
• Implement and evaluate one or more gene expression analysis algorithm.
• Develop algorithms for assessment of performance of genomic analysis methods.
• Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

• Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
• Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

• Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

• Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
• Design and implement computer visualizations of algorithms or data structures.
• Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
• Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

• Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
• Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
• Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

• data structures
• streaming algorithms
• design and analyze data structures / streaming algorithms
• prove impossibility results (lower bounds)
• implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

No longer available for single-term IW  and senior thesis advising, 2023-2024

Opportunities outside the department

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

• Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

• Documentation of historic structures
• Cyber physical systems for structural health monitoring
• Developing virtual and augmented reality applications for documenting structures
• Applying machine learning techniques to generate 3D models from 2D plans of buildings
•  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

• Internet security and privacy 
• Social Networks
• Privacy technologies, anonymous communication
• Network Science
• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

• Research Areas: Memory, the brain and computation 
• Lab:  Princeton Computational Memory Lab

Potential research topics

• Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
• Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

• Apply game theory to sustainability challenges
• Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
• How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

• Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
• Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

• Instrument Princeton's Green (HPCRC) data center
• Investigate power utilization on an processor core implemented in an FPGA
• Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
• Other topics in parallel computer architecture or operating systems

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What 126 studies say about education technology

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In recent years, there has been widespread excitement around the transformative potential of technology in education. In the United States alone, spending on education technology has now exceeded $13 billion . Programs and policies to promote the use of education technology may expand access to quality education, support students’ learning in innovative ways, and help families navigate complex school systems.

However, the rapid development of education technology in the United States is occurring in a context of deep and persistent inequality . Depending on how programs are designed, how they are used, and who can access them, education technologies could alleviate or aggravate existing disparities. To harness education technology’s full potential, education decision-makers, product developers, and funders need to understand the ways in which technology can help — or in some cases hurt — student learning.

To address this need, J-PAL North America recently released a new publication summarizing 126 rigorous evaluations of different uses of education technology. Drawing primarily from research in developed countries, the publication looks at randomized evaluations and regression discontinuity designs across four broad categories: (1) access to technology, (2) computer-assisted learning or educational software, (3) technology-enabled nudges in education, and (4) online learning.

This growing body of evidence suggests some areas of promise and points to four key lessons on education technology.

First, supplying computers and internet alone generally do not improve students’ academic outcomes from kindergarten to 12th grade, but do increase computer usage and improve computer proficiency. Disparities in access to information and communication technologies can exacerbate existing educational inequalities. Students without access at school or at home may struggle to complete web-based assignments and may have a hard time developing digital literacy skills.

Broadly, programs to expand access to technology have been effective at increasing use of computers and improving computer skills. However, computer distribution and internet subsidy programs generally did not improve grades and test scores and in some cases led to adverse impacts on academic achievement. The limited rigorous evidence suggests that distributing computers may have a more direct impact on learning outcomes at the postsecondary level.

Second, educational software (often called “computer-assisted learning”) programs designed to help students develop particular skills have shown enormous promise in improving learning outcomes, particularly in math. Targeting instruction to meet students’ learning levels has been found to be effective in improving student learning, but large class sizes with a wide range of learning levels can make it hard for teachers to personalize instruction. Software has the potential to overcome traditional classroom constraints by customizing activities for each student. Educational software programs range from light-touch homework support tools to more intensive interventions that re-orient the classroom around the use of software.

Most educational software that have been rigorously evaluated help students practice particular skills through personalized tutoring approaches. Computer-assisted learning programs have shown enormous promise in improving academic achievement, especially in math. Of all 30 studies of computer-assisted learning programs, 20 reported statistically significant positive effects, 15 of which were focused on improving math outcomes.

Third, technology-based nudges — such as text message reminders — can have meaningful, if modest, impacts on a variety of education-related outcomes, often at extremely low costs. Low-cost interventions like text message reminders can successfully support students and families at each stage of schooling. Text messages with reminders, tips, goal-setting tools, and encouragement can increase parental engagement in learning activities, such as reading with their elementary-aged children.

Middle and high schools, meanwhile, can help parents support their children by providing families with information about how well their children are doing in school. Colleges can increase application and enrollment rates by leveraging technology to suggest specific action items, streamline financial aid procedures, and/or provide personalized support to high school students.

Online courses are developing a growing presence in education, but the limited experimental evidence suggests that online-only courses lower student academic achievement compared to in-person courses. In four of six studies that directly compared the impact of taking a course online versus in-person only, student performance was lower in the online courses. However, students performed similarly in courses with both in-person and online components compared to traditional face-to-face classes.

The new publication is meant to be a resource for decision-makers interested in learning which uses of education technology go beyond the hype to truly help students learn. At the same time, the publication outlines key open questions about the impacts of education technology, including questions relating to the long-term impacts of education technology and the impacts of education technology on different types of learners.

To help answer these questions, J-PAL North America’s Education, Technology, and Opportunity Initiative is working to build the evidence base on promising uses of education technology by partnering directly with education leaders.

Education leaders are invited to submit letters of interest to partner with J-PAL North America through its  Innovation Competition . Anyone interested in learning more about how to apply is encouraged to contact initiative manager Vincent Quan .

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Exploring the state of computer science education amid rapid policy expansion

Subscribe to the brown center on education policy newsletter, michael hansen and michael hansen senior fellow - brown center on education policy , the herman and george r. brown chair - governance studies @drmikehansen nicolas zerbino nicolas zerbino senior research analyst.

April 11, 2022

• 35 min read

The role of computers in daily life and the economy grows yearly, and that trend is only expected to continue for the foreseeable future. Those who learn and master computer science (CS) skills are widely expected to enjoy increased employment opportunities and more flexibility in their futures, though the U.S. currently produces too few specialists to meet future employment demands. Thus, providing exposure to CS during compulsory schooling years is believed to be key to maintaining economic growth, increasing employment outcomes for individuals, and reducing historical gaps in participation in technology fields by gender and race. Consequently, providing young people with access to quality CS education is increasingly seen as an urgent priority for public school systems in the U.S. and around the globe .

Primary objectives of CS education, as described in the “K-12 Computer Science Framework”—a guiding document assembled by several CS and STEM education groups in collaboration with school leaders across the country—are to help students “develop as learners, users, and creators of computer science knowledge and artifacts” ( p. 10 ) and to understand the general role of computing in society. CS skills enable individuals to understand how technology works and how best to harness its potential in their personal and professional lives. CS education is distinct from digital literacy as it is primarily concerned with computer design and operations, rather than the simple use of computer software. Common occupations that heavily utilize CS skills include software engineers, data scientists, and computer network managers; however, as described below, CS skills are becoming more integral to many occupations in the economy beyond technology fields.

The past decade has been an active period of policy expansion in CS education across states and growing student engagement in CS courses. Yet, little is known about how policies may have influenced student outcomes. This report offers a first look at the relationship between recent policy changes and participation, as well as pass rates on the Advanced Placement Computer Science (AP CS) exams.

Based on our analysis looking over the last decade, we present five key findings:

• We observe sharp, coinciding increases in both state adoption of CS education policies and overall participation in AP CS exams.
• AP CS participation rates for all student subgroups have also increased, with representation gaps between student groups narrowing.
• Narrowing participation gaps for females and especially Black and Latino students have been primarily driven by the introduction of a new AP CS exam (CS Principles), with gaps changing little since then.
• Passing rates on AP CS exams have modestly increased for underrepresented student groups during this period, resulting in slightly narrower passing gaps.
• AP CS student participation overall is associated with increased CS policy adoption, though participation gaps between over- and underrepresented groups appear to be uncorrelated with recent policy adoptions.

Providing universal access to CS education

CS education is undergoing an important transformation in schools. Classes in computing and CS have long been offered in K-12 public schools, though have not been uniformly required, nor universally available. Thus, access to CS has been uneven across student populations. Yet, the growing importance of technological and computing skills in modern society has compelled many school systems to adopt policies to provide universal access to CS education. Several reasons often motivate this expanded access.

First, expanding CS education is expected to directly benefit students. Individuals who develop expertise in computer and technology fields enjoy higher wages and employment. Even those who do not pursue technical occupations still reap these benefits , as computing and data analysis skills have been broadly integrated into many industries and occupations. Finally, CS education also benefits students who do not use computers in their future careers. Prior studies have documented cognitive and interpersonal skills that CS education uniquely provides to students, which transfer outside of computing domains. Moreover, understanding CS fundamentals contributes valuable life skills that prepare and protect students for a future in which many aspects of daily life are carried out in digital contexts.

“The growing importance of technological and computing skills in modern society has compelled many school systems to adopt policies to provide universal access to computer science education.”

Next, economies overall fare better when individuals are more technologically competent. Studies show a positive relationship between economic growth, technology, and human-capital investments in related skills. Many states and countries view computing and technology jobs as engines of economic growth; thus, providing public school students with quality CS education enables sustainable growth. Federal and local politicians often appeal to this economic rationale to justify investments in CS education to public stakeholders— early CS policy-adopter Arkansas is a prime example.

And third, universal access to high-quality CS education is necessary to close historical gaps in technology fields. Black, Latino, and Indigenous populations and women have long been underrepresented in STEM occupations that heavily rely on CS and computing skills . Given the higher wages and job prospects associated with these fields, this underrepresentation of diverse populations in STEM implicitly contributes to race- and gender-based gaps along economic lines. Developing technical skills provides a path to upward social mobility, as has been shown through the assimilation experience of some immigrant groups : Those with computing and other STEM skills reach earnings parity with native workers far faster than those without these skills.

Prior research indicates low access to CS educational opportunities and resources being critical drivers of STEM participation gaps, which tend to mirror larger socioeconomic inequalities based on race, income, or locale. For example, when the only CS offering in a school is an extracurricular robotics club, only those with intrinsic motivation and the resources to participate will gain access to this learning opportunity. Lower access to CS could manifest in various ways from infrequent exposures to computer-based learning applications in the classroom to fewer courses being offered in high schools. Unequal access fails to explain gender-based participation gaps, though these are likely driven by other socialized gender norms that deter girls from computing and other STEM fields . Universal access, however, is expected to both provide CS skills to all students and stimulate greater engagement among underrepresented groups, increasing diversity in STEM occupations.

“Student access to computer science education is highly variable across the U.S.”

Student access to CS education is highly variable across the U.S. Though many schools have provided computer labs and classes in computer literacy (e.g., typing, internet use, word processing), CS courses go beyond basics to provide instruction on computational thinking and other digital operations, and they require teachers with these skills. In many places across the U.S., CS is only offered to students as elective courses or extracurricular activities , if at all. Leaving the provision of CS education to these voluntary contexts leaves the quality of the CS experience highly variable, and dependent on the availability of local resources. Universal access to CS education , however, is expected to standardize learning standards, augment local resource constraints, and ensure equal access to quality instruction.

Enacting CS education policy laws

Calls for universal CS education have been around for years—ranging from corporate efforts and nonprofit advocacy to federal awareness-raising events —though progress has been slow until very recently. Only since 2015 have these efforts yielded the critical mass to push many states to adopt sweeping change in support of CS education.

To illustrate this transformation, consider the policy changes documented through the annual “State of Computer Science Education” (State of CS) reports, co-authored by Code.org Advocacy Coalition, Computer Science Teachers Association, and Expanding Computing Education Pathways. Since 2017, the State of CS reports have promoted and tracked nine different policies intended to promote CS education in schools. 1 The nine policies are:

• whether the state has adopted a formal plan for CS education (abbreviated as State Plan for reporting);
• whether the state has implemented K-12 CS education standards (Standards);
• whether state-level funding is dedicated to CS programs (Funding);
• whether a CS teacher’s certification exists (Certification);
• whether a state-approved pre-service teacher-preparation program for future CS educators is provided at any higher education institutions (Pre-service);
• whether a state-level CS officer exists (State CS);
• whether all high schools are required to offer computer science (Require HS);
• whether a CS course can satisfy a core high school graduation requirement (Count); and,
• whether CS can satisfy a core admissions requirement at state colleges and universities (Higher ed).

In just five years, states showed a remarkable policy transformation; Figure 1 combines and animates this evolution. 2 In the 2017 report, Arkansas was the only state that had adopted at least seven of the nine tracked policies. Meanwhile, 36 states had adopted three or fewer policies, including nine states that had adopted no state-level CS policies at all. But in the 2021 report, 24 states had at least seven policies on the books—a remarkable shift observed across all geographical regions. Only 10 states remain in the lowest adoption category, and all states have adopted at least one policy.

Figure 1 also identifies which policies are adopted. The most commonly adopted policy is having a CS course satisfy a core high school graduation requirement, with all 50 states plus Washington, D.C., adopting it by 2021. Other popular policies include having a state CS plan, funding CS initiatives, creating a state-level CS officer, adopting K-12 CS standards, and recognizing a CS certification for teachers; each of these policy categories counts more than 30 states taking action in the area by 2021.

Providing universal access to CS education in many locales has typically followed the provision of (near) universal access to personal computing devices and broadband. Though some elements of CS fundamentals can be taught without the aid of computers and an internet connection, these are required inputs for a full CS curriculum . Historically, schools and households located in low-income or rural communities have had lower access to digital infrastructure—a phenomenon widely known as the digital divide. Aside from a host of other negative consequences , the implications of this divide on CS education is that students in these contexts have fewer opportunities to regularly interact with computing devices in learning contexts and will have less access to high-quality CS instruction.

More recently, however, the COVID-19 pandemic has acted as a catalyst in making real progress on closing the digital divide. Providing widespread access to needed computing resources has been an urgent priority for many school systems as they have worked to stay connected with students while schools were closed for extended periods. With new devices and ready access to the internet, previously disconnected students are beginning to regularly interact with computers to facilitate their learning. Thus, where some communities may have been less able to offer CS for these reasons in the past, we anticipate that hardware and infrastructure barriers should be less formidable moving forward.

More students are taking AP CS exams

In this active era of CS policy adoption, we explore whether these actions correspond to changes in students’ outcomes in CS. Are students more likely to participate and succeed in CS learning? Do race- and sex-based gaps reduce with more universal access?

To investigate these questions, we use state-level outcomes on the College Board’s AP exams in CS. AP exams are useful outcome measures for this investigation because they are standardized, administered nationally, and represent meaningful competencies in the field that are broadly recognized. This section provides background detail about the AP CS exams.

Situated at the transition point between high school and college, AP courses in multiple subjects are offered in most high schools to advanced students, typically in their final year(s) of high school. Students may opt to take the AP exam at the end of the school year to demonstrate their mastery of the course material. When students matriculate to college, many institutions will award those who passed an AP test with college credits corresponding to an introductory course in the field. Thus, participating in and passing an AP CS exam should probably be considered as a capstone student outcome; that is, one that is realized after multiple years of CS learning opportunities.

Students’ participation in AP courses and exams are widely perceived as important signals of college readiness, and many high schools have expanded their AP course offerings to signal rigor to parents and motivate students. Some scholars question the extent to which participation in AP classes genuinely increases students’ likelihood of college success (since it is primarily advanced students who are enrolling in these courses), and controlling for many student background characteristics sharply diminishes the apparent advantage to AP participation. Other evidence from incentive-driven expansions of AP courses in disadvantaged settings points to AP participation having a causal, positive impact on SAT/ACT scores and college enrollment. Though looking across many studies of the AP program, the academic benefits accrue almost exclusively to those who pass the AP exam (participating in the course without passing the exam provides little, if any, academic benefit).

“Socioeconomically disadvantaged groups lack equal access to AP programming in their schools.”

Even if only those who successfully pass the AP exam benefit, socioeconomically disadvantaged groups lack equal access to AP programming in their schools. In 2014, the Department of Education’s Office for Civil Rights conducted a special data collection on student access to advanced coursework. Reporting shows Black and Latino students account for 27% of those enrolled in at least one AP course and 18% of those passing at least one AP exam, despite these groups accounting for 37% of all students. Further, these gaps are not limited to AP courses but are also evident in advanced STEM courses (like algebra II and physics).

During the years of our investigation, the College Board administered two AP exams covering CS content: Computer Science A (AP CS A) and Computer Science Principles (AP CS P). AP CS A is intended to cover material expected of a first-year CS course in college (with a heavy emphasis on coding), while AP CS P is expected to cover a first-year computing course (including more foundational content such as technology’s impacts on society and understanding how algorithms and networks function). Students in both courses will learn to design a computer program, but only students taking AP CS A will develop the algorithms and code needed for implementation. This does not necessarily mean that AP CS A is more effective—though it is more rigorous and would come after AP CS P in a course sequence. A recent College Board report concludes that students who take AP CS P (relative to those not given the chance) are more likely to take AP CS A in later high school years or declare a CS college major. Though not causal, these findings underscore the importance of AP CS P in developing student interest in the field, particularly among underrepresented student groups.

Of the two exams, AP CS A has a longer history, tracing its origins back to 1984. For much of its history, a modest 20,000 or fewer students would take the exam annually, though these numbers have begun to expand in the last decade. The AP CS P exam, however, was introduced in the 2016-17 school year and has quickly surged in popularity. By spring 2018, its second year of administration, student demand for the AP CS P exam (62,868 public school students) had already surpassed demand for AP CS A (51,645 students).

Figure 2 presents the number of exams taken between 2012-2020 (the most recent year with data available). The first half of the series, AP CS A was the only AP CS exam offered and student demand grew modestly year to year. The AP CS P exam quickly dominated once introduced. In 2020, over 150,000 students took one of these AP CS exams, with nearly two-thirds of that demand coming from AP CS P. For reference, participation in AP exams overall has grown from over 950,000 students in 2012 to 1.21 million in 2020 (27% growth). The surging interest in AP CS exams has significantly outpaced general increases in the other AP subjects.

A recent comparative study of the two AP CS exams finds important differences between students, skill mastery, and intended occupational fields. Students who take the AP CS A exam frequently take several other AP exams and intend to pursue majors in either CS or other STEM fields once in college. Conversely, students taking the AP CS P exam only reported less interest in pursuing CS or STEM majors and careers, and they expressed lower computing confidence (as expected, given the more foundational material).

Further, students who took only the AP CS P were more diverse than those who took AP CS A, though underrepresentation for Black, Latino, and female students is still apparent in both exams. 3 Figure 3 illustrates the differences in diversity between the two AP CS exams. Like the preceding figure, it shows the recent time series of AP test-takers, though instead of numerical counts we are looking at the share of Black and Latino (light blue lines) or female (dark blue lines) test-takers on the y-axis. Black and Latino students constitute between 13-18% of AP CS A test-takers for the entire series but represent 28-30% of AP CS P test-takers. Similarly, female students grew from 18% of AP CS A test-takers in 2012 to 25% in 2020; they constituted an even greater share of AP CS P test-takers during the years it was administered (growing from 30% in 2017 to 34% in 2020).

Throughout the remainder of the report, we combine student results on both AP CS exams and report pooled statistics. We do this primarily for simplicity in reporting, as most outcomes show roughly redundant patterns when analyzed separately by exam; exceptions to this will be noted in the text.

Exploring AP CS outcomes by student race and sex

The AP CS exam results provide two discrete outcomes that we use in the remaining analysis: test-taking and passing. The College Board reports state-level statistics by year and student race and sex for both outcomes, and these will be linked to state policy changes that we described earlier. This section first investigates how the expansion of testing in AP CS evolved through the lens of race and sex representation.

Before proceeding, we should note an important limitation regarding the AP CS exam passing data: When small numbers of students are present in a reported cell, the College Board censors the cell to protect students’ privacy. Cell censoring is common in states with small populations when reporting is broken out by state, year, exam, and race or gender combinations. Consequently, we are constrained in our ability to investigate state policies and their association with passing outcomes by race and sex. We will report some passing rates as pertinent below, though much of the analysis that follows uses test-taking as the primary AP CS outcome.

As discussed previously, increasing racial and gender diversity in CS and related STEM fields is an important motivating factor in adopting universal CS education policies. Have narrowing gaps in AP CS test-taking and passing coincided with the expansion of state-level CS education policies?

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Figure 4 illustrates how differences in representation on AP test-taking have evolved in recent years. The figure is comprised of two animated scatterplots that trace the differences in representation between overrepresented groups on the x-axis (males on the left, white and Asian students on the right) and underrepresented groups on the y-axis (females on the left, Black and Latino students on the right). On both axes are the state’s proportion of each student group represented among test-takers (referenced against the state’s population of 12 th -grade students). 4 Both panels have a 45-degree reference line, marking parity on AP CS test-taking between overrepresented and underrepresented groups. Points falling below this reference line represent test-taking gaps where whites, Asians, and males continue to be overrepresented. A line is also fitted across state observations—points lying on this line share the same relative proportions in the test-taking population between under- and overrepresented groups.

In 2012, the earliest year of the animation, all states are clustered into the bottom left-hand corner of the scatterplots. The position of these points shows low participation overall, and participation is especially low among Black, Latino, and female students. When play is pressed on the animation, the points shift away from the origins, though almost exclusively within the same halves of the plot areas southeast of the reference lines. The fitted line between state observations shows that representation gaps in test-taking have narrowed slightly with time (as the fitted line takes on a steeper slope, moving it closer to parity), though large gaps persist in most states.

Table 1 below provides two key metrics that help to describe how these test-taking patterns by student subgroups have evolved over time. The first metric is the ratio of participation gaps (underrepresented groups/overrepresented groups), which is essentially what the fitted lines in Figure 4 illustrate. A value of 1 represents parity between groups (just as the 45-degree line above has a slope of 1). Participation rates were more than four times higher among male 12 th graders compared to females in 2012, resulting in a participation ratio of 0.24. Increasing female participation in recent years has brought them closer to parity with a 2020 value of 0.46. Table 1 also reports the difference in the share of test-takers from overrepresented groups less underrepresented groups, where a value of 0 represents a 50-50 split in test-takers’ demographics. In 2012, AP CS test-takers were just under 20% female, and just over 80% male, resulting in a test-taking share gap exceeding 62 percentage points. This gap has narrowed to less than 40 percentage points as of 2020. Similar patterns of progress are shown on race-based metrics.

Table 1 shows both the participation ratios and test-taking share gaps calculated by sex and race for three selected years: the first year of data (2012), the year AP CS P was introduced (2017), and the final year (2020). Examining how these metrics have changed over the series is instructive: Much of the overall improvements in the metrics were realized in 2017 with the introduction of the AP CS P exam. Progress made in the years since has been more modest in comparison, and the gains have been larger on sex gaps rather than racial gaps.

We find other encouraging patterns of narrowing gaps when focusing on AP CS passing rates. When rapidly expanding the test-taking pool, one might be concerned that students who are induced to take the AP CS exams will not be as prepared for the exams as those students who had already prepared for AP CS before the expansion. This concern resonates especially for the AP CS P exam, which has expanded dramatically to more than 100,000 exams taken annually in just a few years. To the contrary, though, our analysis of the data suggests that passing rates among underrepresented groups have increased during this period of AP CS expansion and increased faster than those among overrepresented groups.

Figure 5 presents the passing rates on AP CS exams by sex (on the left) and race (on the right) over recent years. The x-axes represent years and the y-axes represent the passing rates for each student group; passing rates are pooled across both AP CS exams. In both panels, the overrepresented groups are passing the exams at higher rates, and an especially large margin is apparent between racial groups. Yet, during these years of participation growth, passing rates among underrepresented groups simultaneously increased. Meanwhile, the passing rates for overrepresented groups (males on the left, whites and Asians on the right) inched upward during this period of expansion. On net, the gaps between these groups narrowed, and female passing rates overtook that of males in 2020.

To confirm that the narrowing gaps depicted in Figure 5 are not simply driven by the surging popularity of the AP CS P exam, we separately investigated passing rates on each of the AP CS exams. The narrowing gaps observed in Figure 5 are also observed in each test. For example, female passing rates on the AP CS A exam increased from 56% (2012) to 68% (2020), and they increased on the AP CS P exam from 70% (2017) to 75% (2020). Increases of 5 or more percentage points were similarly observed among Black and Latino test-takers on both tests during this period. Meanwhile, the passing rates among overrepresented groups increased slightly on the AP CS A exam over the period, while dropping slightly on the AP CS P exam. Again, the net results showed narrowing gaps for underrepresented groups both by race and sex on both exams.

Associating CS education policy changes with AP test-taking

Finally, we explore whether states that are making more progress on their CS education policies show more favorable outcomes on AP CS exams. For example, it’s possible that those states taking more policy actions to improve universal access to CS education have seen greater uptakes in AP CS participation or sharper reductions in underrepresented gaps when compared with those states doing little.

Before discussing our results, though, we must acknowledge that policy adoption metrics are imperfect proxies for practice. The State of CS reports are careful to note that state policies vary widely, even within the same policy categories. Further, a state may decide to adopt a given CS education policy, but implementation may be thwarted by barriers that curtail its practical impact. Other states may put CS-enhancing practices into place even in the absence of a formalized state policy. This difficulty can be seen in Figure 6, which represents the differences in observed practices under three different policy-status categories. Figure 6 focuses on the percentage of high schools in a state offering foundational CS courses (y-axis), a practice that provides more universal access to CS for all students. The State of CS policy corresponding to this action is whether states have a policy requiring all high schools to offer CS (Require HS). The x-axis separates those states that have no policy, those that have adopted a policy with a target implementation goal in the future (in progress), and those with the policy already in force (yes).

The box-whisker plots represent the means and distributions of states observed within each of the three policy-status categories. Those states with a state policy in force have the highest mean percentage of high schools offering CS, and those with the policy in progress have higher percentages than states with no policy. Yet, the observed differences in practice across states are far smaller than the policy-status variables alone would indicate. The key point here is that we are constrained to look at the data available to us on policy status, not actual practices; consequently, we may be failing to capture important differences in practice in our analyses.

To conduct the analysis, we merged the State of CS policy adoption data with the AP CS exam data by state and year. 5 We ran a series of two-way fixed-effects models, which are intended to net out other correlated changes in test-taking behavior observed within the state over time and across other states contemporaneously. We ran a separate model on each of the nine tracked CS policies and looped this operation across different test-taking metrics as dependent variables. The results of this exercise are presented in Table 2 below.

The columns of Table 2 correspond to different analytical models in which the outcomes of interest are the overall test-taking rate (column 1) as well as the percentage of test-takers that are female (column 2) and Black or Latino (column 3). The nine CS policies are represented down the row headings. The cell corresponding to a row-column combination represents the point estimate and standard error of a two-way fixed-effects model with the policy in the row heading being used as the explanatory variable and the student group in the column heading as the output of interest. Cells are color coded for ease of interpretation to highlight where the estimates are largest.

The high-level summary of the Table 2 results is that several of these CS education policies are positively associated with AP CS test-taking behavior among students overall. The first column shows the largest and most statistically significant estimates correspond to policies that 1) allocate state funding for CS education initiatives, 2) require state colleges to recognize CS courses as STEM courses in admissions decisions, and 3) require all high schools in the state to offer CS courses. We are generally unsurprised at this result, as all three of these policies feasibly have a direct impact on late-high-school students, which are the target population for AP CS exams. Other policies, like offering a teacher certification program in CS education or having a state-level officer responsible for CS education, would likely influence these late-high-school outcomes through more indirect means.

Another finding from Table 2 is that none of the policies seem to be associated with a relative increase in the proportion of test-takers from underrepresented groups. Only one point estimate is significant in column 2 (whether a CS course counts toward a STEM graduation requirement), and it is in the direction of widening the sex-based gap. This result must be taken with a grain of salt because this policy (Count) was primarily adopted in the earlier years of the past decade when gaps were at their largest. A crucial factor driving these estimates is the (almost) constant proportion of underrepresented test-takers between 2018 and 2020, the years for which we have an overlap of policy implementation and AP test-taking data.

We should also note that with the high levels of state policy activity coinciding with a rapid expansion of AP CS test-taking, we cannot claim that any of the point estimates reported in Table 2 represent a causal relationship. Rather, this is our best attempt to isolate associations that are unique to certain policy-outcome combinations to explore the relationship; results are not intended to be definitive evaluations of any given policy.

Even if the expansion of these CS policies had little apparent relationship with test-taking gaps overall, this does not mean that that was the experience of students in all states. We wish to explore whether surges in the performance of underrepresented groups accompanied CS policy expansions in any state, and we do this in the map presented in Figure 7.

Figure 7 presents a bivariate map of the U.S., where states are color coded based on observed changes in two directions: growth in state-level CS education policy adoption and growth in Black and Latino AP CS test-taking rates. States above the median on both dimensions are shaded in dark blue, and states below the median on both are shaded in light gray. The light blue and dark gray shades represent states high on one dimension or the other, but not both.

This analysis reveals some surprising geographical differences. Using the Mississippi River as the dividing line, nearly all states with the highest increases in test-taking among Black and Latino student groups are east of the river (Nevada and Montana are the only exceptions west of the Mississippi). And among the states with the highest test-taking increases in the East, states are split about evenly between high and low policy-adoption categories. Contrast this pattern against states west of the Mississippi, where nearly all states are in the low-growth category for Black and Latino AP CS test-taking, with over two-thirds of those are in the low-growth policy category.

Reflecting on the map leaves us with two important lessons. First, the map vividly illustrates that policy adoption itself is not an accurate predictor of stronger outcomes for underrepresented groups. We observe many states with high policy growth that see comparably little improvement in test-taking outcomes for Black and Latino students; meanwhile, we also see many examples with high growth among Black and Latino students that did not display the same aggressive levels of policy adoption.

“Policy adoption itself is not an accurate predictor of stronger outcomes for underrepresented groups.”

And second, the map suggests that geographical commonalities may be an important lever supporting CS student outcomes. It is unclear from this analysis how those geographical relationships will matter, but this offers some useful direction for future work. A suggestive clue comes from the 2021 State of CS report ( p. 14 ), which shows a policy map of the percentage of schools offering foundational CS, with a similar East-West divide evident. We confirm that the percentage of high schools offering CS at the state level is also positively correlated with both our measure of policy growth and increasing Black and Latino participation. Though merely suggestive, more universal high school CS offerings presents a clear mechanism through which greater shares of underrepresented groups will be exposed to CS instruction, and therefore participate in meaningful coursework leading to AP CS exams.

Concluding discussion and recommendations

We investigated CS education policy adoption and AP CS exam outcomes in recent years—both of which saw rapid expansion during this time. We found gaps modestly narrowing for historically underrepresented student groups in CS and STEM fields, though much of the narrowing was associated with the introduction of the AP CS P exam. Our further investigations made it clear that overall participation rates on AP CS exams appear to be associated with CS policy adoptions, though none of these policies show any clear relationship with increasing the share of historically underrepresented groups among test-takers.

We recognize that some of these findings cut against a dominant narrative in CS education circles, which states that increased access to CS education will lead to narrowing participation gaps. While we do find gaps narrowing in recent years, these do not appear to be related to policy adoption. We clarify, however, that these results are based on a narrow dataset immediately in the wake of policy changes. These findings are not observed over long periods of implementation nor on a broad set of outcomes, which could counter these early patterns. For example, recall from our earlier discussion that white and Asian students are more likely to enroll in a richer set of STEM and AP-level courses generally , and they are more likely to engage in CS courses specifically . It seems probable that, as states kickstart CS education initiatives, the overrepresented student groups that enjoy preferred access may be better positioned to take advantage of newly available opportunities. Similarly, more fundamental outcomes like student exposure to coding or discussions of new technology in class (which contrast with the capstone AP CS outcomes in our data) may be more likely to have a disproportionate impact on underrepresented groups, narrowing formative exposure gaps. In either case, it seems plausible that narrowing CS and STEM participation gaps over a period of several years of policy implementation may still result even if AP CS gaps appear to be uncorrelated with short-term policy changes.

“Even as AP computer science test-taking has increased among underrepresented groups, the passing rate has also increased, resulting in narrower gaps with overrepresented students.”

Our results also provide some unambiguously encouraging news. First, even as AP CS test-taking has increased among underrepresented groups, the passing rate has also increased, resulting in narrower gaps with overrepresented students. Also, even states that have not been as active in promoting CS education policies have still shown large surges in AP CS participation; thus, even in the absence of policy action, we see reason to be optimistic about the trajectory of CS education overall.

We hope these findings invite reflection and re-evaluation of how states are approaching the expansion of CS education. As we close, we offer the following recommendations to state education agencies and policymakers working to expand CS education:

• Track multiple dimensions of CS education. CS is unique among academic disciplines in that it has previously been offered as an elective, but it is becoming more integrated into the academic core curriculum. Consequently, we do not have systematic measures in place tracking student competencies, access to coursework, teacher quality, or other similar outcomes as we do for core academic disciplines. More consistent measurement of inputs and outputs will help to steer states’ actions in CS.
• Prioritize diversity and inclusion in implementing CS policies. The oft-invoked link between expanding universal access to CS education and narrower participation and interest gaps in CS and STEM does have some empirical support, but certainly not enough to conclude that one necessarily leads to the other. Leaders and educators must ensure CS policies are implemented in inclusive ways to increase the chances of narrowing these persistent gaps. We encourage attention to both the classroom experiences of underrepresented student groups and CS educator diversity, too, as race- and gender-based role modeling are important predictors of future interest in CS and STEM .
• Take the long view on CS implementation. This report documents a flurry of activity around CS education in recent years, though we also urge patience and strategy here. Many states are still building the capacity to offer high-quality CS education—perhaps not so much in terms of physical capital (devices and broadband infrastructure), but more so in human capital (building capacity in the teacher workforce and scaling up high-quality instruction). By nature, these investments will take time to mature before students fully realize the benefits. We should not be discouraged by lackluster immediate results.

Computing and technology will be integral parts of the economic and social future awaiting the children of today. Providing access to high-quality CS education will be key in ensuring that all students can meet that future head on.

The authors thank Logan Booker and Marguerite Franco for excellent research assistance, and Nicol Turner Lee, Pat Yongpradit, and Jon Valant for helpful feedback.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Howmet Aerospace Foundation. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

• The nine policies that the State of CS annual report tracks were first described in a Code.org policy document, “ The Nine Policy Ideas to Make Computer Science Fundamental to K-12 Education ,” (n.d.), though we do not know of the policies being systematically tracked until the first State of CS report in 2017. The 2017 report included a 10th policy on promoting diversity in CS education, though this policy was dropped in subsequent years.
• The State of CS report counts states that have CS policies in progress (that is, the policy decision has been passed or issued, though the policies have a target implementation date in the future) as earning a half point on their policy tracker. Policies that have been passed and are implemented earn a full point. For ease of interpretation, we counted both implemented policies and policies in progress as earning a full point.
• We focus on Black, Latino, white, and Asian students because other racial/ethnic groups are inconsistently recorded over the time series; they represent roughly 90-95% of observations across years. Our results are qualitatively similar if we include other underrepresented racial/ethnic groups in the calculations.
• Student demographic information on 12th graders comes from the Department of Education’s Common Core of Data. Not all students taking an AP exam will be 12th graders, but we use their demographics as a baseline due to the tendency of younger cohorts of students to become progressively more racially diverse with time.
• This merging process results in three years in which we have observations of both CS education policies in place and AP CS outcomes (2018, 2019, and 2020). Because some of the policies documented in the 2017 State of CS report may not have been passed and implemented before the AP CS administration in the spring of that year, we lag all of the State of CS reports back one year before merging with AP CS exam results.

Education Policy K-12 Education

Governance Studies

Brown Center on Education Policy

Sofoklis Goulas

March 14, 2024

Melissa Kay Diliberti, Stephani L. Wrabel

March 12, 2024

Sarah Reber, Gabriela Goodman

• Open access
• Published: 06 September 2023

Game-based learning in computer science education: a scoping literature review

• Maja Videnovik   ORCID: orcid.org/0000-0002-9859-5051 1 ,
• Tone Vold   ORCID: orcid.org/0000-0003-4850-3363 2 ,
• Linda Kiønig   ORCID: orcid.org/0000-0001-8768-9370 2 ,
• Ana Madevska Bogdanova   ORCID: orcid.org/0000-0002-0906-3548 3 &
• Vladimir Trajkovik   ORCID: orcid.org/0000-0001-8103-8059 3  

International Journal of STEM Education volume  10 , Article number:  54 ( 2023 ) Cite this article

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Using games in education has the potential to increase students’ motivation and engagement in the learning process, gathering long-lasting practical knowledge. Expanding interest in implementing a game-based approach in computer science education highlights the need for a comprehensive overview of the literature research. This scoping review aims to provide insight into current trends and identify research gaps and potential research topics concerning game-based learning in computer science. Using standard methodology for scoping review, we identified 113 articles from four digital libraries published between 2017 and 2021. Those articles were analyzed concerning the educational level, type of the game, computer science topic covered by the game, pedagogical strategies, and purpose for implementing this approach in different educational levels. The results show that the number of research articles has increased through the years, confirming the importance of implementing a game-based approach in computer science. Different kinds of games, using different technology, concerning different computer science topics are presented in the research. The obtained results indicate that there is no standardized game or standardized methodology that can be used for the creation of an educational game for computer science education. Analyzed articles mainly implement a game-based approach using learning by playing, and no significant focus is given to the effectiveness of learning by designing a game as a pedagogical strategy. Moreover, the approach is mainly implemented for developing computational thinking or programming skills, highlighting the need for its implementation in other topics beyond programming.

Introduction

The world is changing very fast due to the emergence of technology in our everyday lives. This tremendous change can be noticed in different areas, including education. Students are influenced by the digital era, surrounded by technology and working with a massive amount of digital information on an everyday base. They are used to interactive environments and fast communication and prefer learning by doing (Unger & Meiran, 2020 ). Traditional learning environments, where students should sit and listen to the information provided by the teachers are unacceptable for them (Campbell, 2020 ). Students require active learning environments, using the possibilities of various technology applications to gain knowledge. They seek more interesting, fun, motivating and engaging learning experiences (Anastasiadis et al., 2018 ).

Creating engaging learning environments can develop students' critical thinking, problem-solving skills, creativity and cooperation, preparing students for living in a constantly changing world (Joshi et al., 2022 ; Lapek, 2018 ; Tang et al., 2020 ). Education needs to shift toward active learning approaches that will encourage students to engage on a deeper level than traditional lecture-based methods (Boyer et al., 2014 ). To achieve this, teachers must find an approach tied to digital tools that students use daily (Videnovik et al., 2020 ).

Implementation of a game-based learning approach for creating engaging learning environments

Game-based learning is considered one of the most innovative learning approaches for increasing students' interest in education by playing games (Priyaadharshini et al., 2020 ). It refers to using games as an educational tool or strategy to facilitate learning and engagement (Li et al., 2021 ). Game-based learning involves designing and incorporating educational content within a game format, where players actively participate and interact with the game mechanics to acquire knowledge or develop skills. Many approaches tackle the umbrella of application of game-based learning in different educational fields. Different playful experiences can enable children to construct knowledge by playing and exploring a real-world problem often driven by students’ interest in inquiry (Hirsh-Pasek, 2020 ). Gamification is a process that uses game elements, such as points, rewards, badges and competition during the learning process, establishing interactive and engaging learning environments (Turan et al., 2016 ). Gamification aims to enhance motivation, engagement, and participation using the inherent appeal of games. Designing interactive and entertaining games, primarily for education, is a step forward in implementing game-based learning. Serious games enable players to cultivate their knowledge and practice their skills by overcoming numerous interruptions during gaming (Yu, 2019 ). Effectively designed serious games facilitate learning by stimulating creativity, igniting interest, promoting discourse, and cultivating a competitive drive for exploration in diverse fields. Different mobile and location-based technologies provide opportunities to embed learning in authentic environments and thereby enhance engagement and learning outside traditional formal educational settings (Huizenga et al., 2009 ). Those games can simulate various aspects of reality, such as driving a vehicle, managing a city, or piloting an aircraft, allowing players to experiment and make decisions in a safe space without real-world consequences (Toh & Kirschner, 2020 ).

Games enable the integration of intrinsic and extrinsic motivational components to create an environment, where players feel more motivated to engage in the activities (Hartt et al., 2020 ). When digital game-based learning is implemented, including key game design elements (collaboration, choice, feedback), there is typically a positive impact on student engagement (Serrano, 2019 ; Wang et al., 2022 ). Students approach gameplay with interest and dedication and are persistent in progressing it. Therefore, teachers must find different ways to implement a game-based approach in the classroom, utilizing students' engagement, persistence and motivation during gameplay for classroom activities. During game-based learning, students have fun and enjoy themselves with increased imagination and natural curiosity, which can lead to high levels of participation and the student's involvement in the learning process. In this way, students can be more successfully engaged in meaningful learning than traditional teaching methods (Hamari et al., 2016 ; Huizenga et al., 2009 ; Karram, 2021 ).

Research on using a game-based learning approach in education

In the last decade, the game-based approach is receiving increasing attention in the research community due to its potential to increase students' motivation and engagement, promoting a student-centred learning environment. Many researchers show that digital game-based learning is becoming a powerful tool in education, making learning more enjoyable, easier and efficient (Boyle et al., 2016 ; Hafeez, 2022 ). Implementation of a game-based learning approach can provide students with an engaging, motivating and stimulating environment (Ghergulescu & Muntean, 2012 ; Hwang et al., 2014 ), supporting them to focus on the task and increasing overall learning experiences (Hamari et al., 2016 ). Moreover, game-based learning has the potential to improve students’ competencies and academic performance (Clark et al., 2016 ; López-Fernández et al., 2021a , 2021b ; Mezentseva et al., 2021 ; Noroozi et al., 2020 ; Sanchez Mena & Martí-Parreño, 2017 ; Vu & Feinstein, 2017 ). It presents the learners with rich, immersive environments and experiences that are not just about learning facts but enables the development of problem-solving, decision-making, and strategic planning (Lymbery, 2012 ; Sung & Hwang, 2013 ) skills. In addition, the student's academic achievement using a game-based approach is better than those learning through the traditional method (Arcagök, 2021 ; Partovi & Razavi, 2019 ; Roodt & Ryklief, 2022 ; Wang et al., 2022 ). Educational games promote active and self-directed learning, enabling students to learn from authentic situations and receive immediate feedback (Pellas & Mystakidis, 2020 ; Zhao et al., 2021 ). It can be highly personalized, allowing students to learn at their own pace and in a way best suited to their individual needs and learning styles, engaging them in the self-assessment process (Videnovik et al., 2022 ). In a gaming environment, students can explore different scenarios, make choices, and learn from the consequences of their actions without fear of making a mistake.

Despite the great potential of the game-based approach for learning, it must be noted that developing educational games can be very complex and costly, and faces significant challenges (Boyle et al., 2016 ). The process of designing an educational game needs a lot of planning and requires a lot of skills (Hussein et al., 2019 ). Teachers do not have necessary skills to develop a game that combines entertainment and educational elements to increase student's interest and motivation during learning (Qian & Clarck, 2016 ). On the other side, game developers have problem to align educational goals within the game. In addition, the games must be well-designed and with the right level of complexity so the learners should not be bored or frustrated during the play (Liu et al., 2020 ; Vlahu-Gjorgievska et al., 2018 ), taking into account both educational and entertainment elements. That is why educators cannot depend solely on professional game designers and must take on the responsibility of creating these immersive learning experiences themselves or by engaging their students in the design process.

Game-based learning approach in computer science education

The game-based approach provides a dynamic and effective way for students to learn and apply their knowledge in a variety of subjects, such as math (Vankúš, 2021 ), physics (Cardinot & Fairfield, 2019 ), languages (Lee, 2019 ), and history (Kusuma et al., 2021 ). This approach allows students to learn complex concepts and skills in a fun and interactive way while also fostering critical thinking and collaboration. It is particularly effective in computer science, where students can learn about algorithms, data structures, networks, software testing and programming languages by designing and testing their games and simulations (Kalderova et al., 2023 ). In addition, game-based learning can help to bridge the gap between theory and practice, allowing students to apply their knowledge in a real-world context (Barz et al., 2023 ).

The importance of computer science has been emphasized in the last decade through different campaigns and online platforms. Their main aim is to develop students' computational thinking skills and attract students to coding, mainly through a game-based approach (code.org, codeweek.org). They offer teachers access to materials and learning scenarios covering different unplugged activities and block-based programming. Students have an opportunity to play games and learn basic programming concepts through fun and interactive activities, developing collaboration and competitiveness at the same time. Game narratives, collecting points, and immediate feedback through these games increase students’ engagement. These platforms are a valid option for developing computational thinking at an early age and a good way for students to develop creativity, critical thinking and problem-solving skills (Barradas et al., 2020 ).

Various block-based programming languages, which are also accessible online (Scratch, Footnote 1 Snap, Footnote 2 Blockly Footnote 3 ), are used to develop students' computational thinking and block-based programming skills, especially in primary education. In addition, they support the development of interactive projects that students can use afterward (Tsur & Rusk, 2018 ). Moreover, students can develop animations, interactive stories, and games, which allow them to engage in the coding process, learn programming concepts and even learn about other computer science topics during game design.

Topics connected with programming are the most common in computer science, but learning how to program is often recognized as a frustrating activity (Yassine et al., 2018 ). Learning object-oriented programming languages is especially difficult for students, because programming concepts are complex, cognitively demanding, require algorithmic thinking and problem-solving skills, and is a long-term process (Zapušek & Rugelj, 2013 ). Game-based learning stimulates active learning and enables students to learn about programming concepts in fun and engaging ways through visual interfaces and engaging environments (CodeCombat, Footnote 4 Alice, Footnote 5 Greenfoot Footnote 6 ). Those engaging and motivating environments enable simplifying complex programming concepts, such as inheritance, nested loops, and recursion (Karram, 2021 ).

Different pedagogical strategies can be used to implement game-based learning in computer science, empowering students' skills and increasing their active engagement in learning. For example, students can deepen their knowledge and skills on a given topic by playing the game (Hooshyar et al., 2021 ; Shabalina et al., 2017 ) or through the process of game design (Denner et al., 2012 ; Zhang et al., 2014 ). In both cases, the game-based approach can increase students' motivation and engagement in learning (Chandel et al., 2015 ; Park et al., 2020 ).

Existing reviews of game-based approach in computer science

Existing reviews of game-based approach in computer science provide valuable information about the latest trends in the implementation of game-based approach in the last few years. Table 1 presents latest trends in the implementation of game-based learning in computer science education.

Most of the review articles analyze publications that describe the implementation of game-based approach for learning programming (Abbasi et al., 2017 ; Diaz et al., 2021 ; Dos Santos et al., 2019 ; Laporte & Zaman, 2018 ; Shahid et al., 2019 ), from different aspects: game design, game elements, or their evaluation. However, there are some of them tackling other topics, such as cybersecurity (Karagiannis et al., 2020 ; Tioh et al., 2017 ) or cyberbullying (Calvo-Morata et al., 2020 ). Sharma et al. ( 2021 ) analyzes the impact of game-based learning on girls’ perception toward computer science. There are review articles that focus on just one aspect of computer science. For example, Chen et al. ( 2023 ) provides meta-analyses to investigate potential of unplugged activities on computational thinking skills.

In our review, we aim to perform the broader analysis of the research articles referring to the game-based approach in various computer science topics, different educational levels and different types of games. For that purpose, instead of systematic review, we have opted to perform the scoping review on significantly larger set of articles.

Valuable insight regarding the game-based approach in computer science has been provided in research concerning different educational levels, computer science topics, and used games. However, computer science is a field that is changing very fast, and the number of games that can be used for developing students' knowledge and skills is increasing all the time. As a result, continuous research in this field should be done.

This research aims to elaborate on current trends concerning the game-based approach in computer science. It focuses on the educational level, covered computer science topic, type of the game, purpose for its use, and pedagogical strategies for the implementation of this approach. Moreover, possible gaps and potential research topics concerning game-based learning in computer science in primary education are identified.

Current review

This research represents scoping review that identifies the educational context and the type of games used for implementing a game-based learning approach in computer science. The scoping review method was selected over systematic literature review, because we wanted to determine the scope of the literature in the field of game-based learning in computer science education, to examine how research is done on this topic and to identify and analyze research gaps in the literature (Munn et al., 2018 ).

Following Arksey and O’Malley ( 2005 ) five-step framework, which adopts a rigorous process of transparency, enabling replication of the search method and increasing the reliability of the results, the steps of the applied review process are: to (1) identify research questions (2) identify relevant studies, (3) study selection of papers, (4) charting the data, (5) summarizing and reporting the results.

Research questions

The focus of our research was to analyze what type of games were used in computer science, the subject's topics that were covered by the game and pedagogical strategies for implementing game-based learning, comparing all these in different educational levels. Starting from this, our research questions are:

RQ1: What kind of educational games are usually used during the implementation of the game-based approach in computer science?

Various games are used to cover topics from computer science, from block-based serious games (Vahldick et al., 2020 ) to educational escape rooms (López-Pernas et al., 2019 ). Using different games influences the learning process differently (Chang et al., 2020 ). The RQ1 seeks to identify and understand the types of educational games that are commonly utilized in the context of teaching computer science. Exploration of the variety of used games provides insights into the different approaches, mechanics, and formats used to enhance learning outcomes.

RQ2: Which pedagogical strategy is mostly used in the published research?

There are various strategies for implementing game-based learning in computer science education. The implementation strategies refer to whether students should learn by playing the game (Malliarakis et al., 2014 ) or by designing a game (Denner et al., 2012 ). The strategies can differ based on the gender of students (Harteveld et al., 2014 ), students' age (Bers, 2019 ), or the adopted approach by policymakers (Lindberg et al., 2019 ). RQ2 aims to identify the predominant pedagogical strategy employed in the published research on game-based approaches in computer science education. By examining the pedagogical strategies, researchers can gain insights into the most effective instructional methods that facilitate learning through game-based approaches. Furthermore, the findings can inform educators and researchers in designing and implementing effective instructional strategies that align with the goals of computer science education.

RQ3: Which computer science topics are covered by the game-based approach?

Game-based learning can be used to teach different computer science topics, from introduction topics (Fagerlund et al., 2021 ; Mathew et al., 2019 ), to core topics (Karram, 2021 ). RQ3 aims to provide value in exploring the specific computer science topics addressed through game-based approaches. In addition, it helps identify the range of topics that have been integrated into educational games. By understanding the computer science topics covered, researchers can assess the breadth and depth of the game-based approach and identify potential gaps or areas for further exploration in the curriculum.

RQ4: What are the potential research topics concerning the implementation of a game-based approach in computer science?

RQ4 is essential as it seeks to identify potential areas for future research in the implementation of game-based approaches in computer science education. It might include specific computer science topics (Calvo-Morata et al., 2020 ), strategies to implement game-based learning in computer science (Hooshyar et al., 2021 ), or ways to analyze the effects of game-based learning (Scherer et al., 2020 ). By exploring research topics that have not been extensively studied or require further investigation, researchers can identify new directions and opportunities for advancing the field. This can contribute to the ongoing development and improvement of game-based approaches in computer science education, fostering innovation and addressing emerging challenges.

Methodology

To answer research questions, we analyzed the contents of articles published from 2017 to 2021. Due to the rapid development of technology and change in the learnt computer science topics as well as designed game with new technology and tools, we have decided to research the articles that refer just to the interval of 5 years. As technology progresses swiftly, studying 5 year interval of the published literature ensures that scoping review results analyze the most current tools, approaches, and methodologies being utilized in the field of computer science education.

The research was done according to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines (Peters et al., 2020 ). The PRISMA-ScR methodology is a structured approach used to conduct comprehensive and transparent scoping reviews. It involves identifying a research question, performing a systematic search of relevant literature, applying inclusion and exclusion criteria to select studies, extracting data from the included studies, analyzing and synthesizing the data to identify key themes or patterns, and reporting the findings. It aims to map the existing literature on a particular topic, identify key concepts, and examine the extent, range, and nature of research available. It is particularly useful for exploring complex and diverse research questions.

There is a large number of articles regarding the topic, so performing this kind of research manually seemed like labor-intensive work. Therefore, we have identified the opportunity to use the Natural Language Processing (NLP) toolkit (Zdravevski et al., 2019 ) to automate the literature search, scanning, and eligibility assessment. We have used this toolkit for article identification and selection (i.e., scanning procedures and eligibility criteria assessment). The search considered articles indexed in four digital libraries: IEEE, PubMed, Springer and Elsevier. The NLP toolkit requires structured data input comprising keywords, properties, property groups, required relevance, included sources, and start and end years.

The provided keywords serve as search criteria within available libraries, acting as the primary filter to determine which articles will be gathered for further analysis. At the beginning of setting up the NLP toolkit for the research, to address different games that can be used in education, we have identified the main keywords to be "Serious Games", "Educational Games", "Games in education" or "Games for learning". The NLP toolkit used these keywords to identify the potentially relevant articles in the mentioned digital libraries.

Furthermore, the NLP toolkit was adjusted to search specific properties (words or phrases) within the title, abstract, or keywords of already identified articles to select relevant articles in more detail, according to the features (properties groups) of the game-based learning approach that we are interested in: subject, educational level, educational context, purpose and used technology. Properties groups address synonyms and various versions of the phrase (e.g., educational games and serious games). To be included in the results, at least one representative from each property group must appear in the title or abstract of the article, thereby functioning as a secondary filter for identifying relevant articles.

The property group "subject" was set as mandatory during the search, because we were interested in analyzing articles that refer to game-based learning just in computer science. Since the name of this subject is different in different countries, we have used synonyms, such as "programming", "coding", and "informatics". The property group "age" or educational level included different synonyms for primary and secondary education, as well as higher education, although we did not make this property mandatory. To search about the used technology (web, online, mobile, augmented reality, virtual reality), we have set one property group to include a different kind of used technology, and we also set a property group that refers to the aim of using these educational games (to achieve students' engagement, increase motivation, evaluation of educational results, etc.). A more detailed description of the properties groups is given in Table 2 .

The following input parameter for the NLP toolkit set-up is the minimum relevant properties. In this research, it was set that each article has to contain a minimum of two of the previously defined properties to be considered relevant. The quality analysis of the relevant articles followed in the next step of the methodology.

Study selection

The initial search in four digital libraries: IEEE, PubMed, Springer and Elsevier, has identified 43,885 articles concerning using game-based learning in computer science. After articles had been identified based on the specified keywords and retrieved from the publishers, the duplicates were identified according to the article DOI as their unique identifier and removed, which has decreased the number of articles to 21,002. In the next step, the articles selection (screening and eligibility assessment) procedures followed, discarding articles not published in the required period or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or other reasons. The screening process eliminated 11,129 articles and the remaining 9873 articles underwent an automated eligibility assessment using the advanced NLP toolkit functionalities. The automated eligibility analysis involved the following processing: tokenization of sentences (Manning et al., 2014 ; Webster et al., 1992 ) and English stop words removal, stemming, and lemmatization using the Natural Language Toolkit library (Bird, 2006 ). Furthermore, articles containing less than two properties were removed, which left 1209 articles eligible for further manual analysis and inclusion in identifying the research trends and summarizing the results.

For each of the articles from the collection of relevant articles, the toolkit automatically generated a bibliographic file (as defined by BibTeX reference management software). This file was manually analyzed in more detail to identify the most relevant articles for the purpose of our study. First, the abstract was read to see whether the article was relevant, and if that did not provide enough information, the whole article was read. For each of the research questions we used the same approach, but with different focuses. For the first research question, we looked for any specific game name. For the second research question, we were looking for any mentioning of the pedagogical approaches or strategies. For the third research question, we looked for different computer science topics used in computer science curricula. In that way, the most relevant articles concerning first three research questions were identified. The last research question is related to future potential research topics in the field of game-based learning in computer science education, so it was not used during this phase of selection of relevant articles.

As a result of the manual analysis of articles’ titles, articles that did not refer to computer science subjects were excluded, which left just 206 articles. We could not obtain the full text for some of articles, so they were excluded from further analyses. Some articles did not refer to using games to teach computer science topics, so they were also removed. The same was the case with a few articles not written in English. Finally, we had 125 relevant articles.

Nine relevant articles were review papers that referred to different game-based learning approaches at different educational levels. Among identified articles is a book describing different teaching methods in computer science education, including game-based learning (Hazzan et al., 2020 ). Two book chapters refer to different approaches of using game-based learning in education (Bellas et al., 2018 ; Zaw & Hlaing, 2020 ). These articles were also excluded from the list.

Finally, we finished the selection process and got 113 relevant articles using educational games in computer science that were the subject of further analysis.

The information flowchart presenting the numbers of identified, screened, processed, and removed articles in the automated NLP procedure and articles removed during the manual analysis is presented in Fig.  1 .

Flowchart of the PRISMA-SCR-based selection process

After the final identification of the most relevant studies concerning game-based learning in computer science, summaries were developed for each article. Information about their correspondence to education, educational level, used game, type of the game, covered computer science topic, educational context and general usefulness of the article was provided.

Distribution of published articles through the years

The distribution of the articles concerning the game-based approach in computer science through the years is presented in Fig.  2 . It can be noticed that the number of articles was increasing through the years, but then suddenly, in 2021, that number decreased. The reason might be found in the situation with the pandemic, because in 2020 and 2021, most of the schools were closed. In some of them, the teaching was transferred online, which resulted in a huge change in the way of teaching and learning, and it was a period of adaptation for teachers and students at the same time, which might lead to a decrease of the research articles.

Distribution of the published articles through the years

Distribution of published articles per country

The distribution of the published articles per country differs from country to country. Figure  3 presents the distribution of published articles per country, showing only the countries that have more than five published articles concerning game-based learning between 2017 and 2021. Most articles are published in the United States, followed by Brazil and Greece.

Distribution of the published articles per country, showing countries with more than five published articles

Further analysis of the relevant articles depending on the country, where the research was conducted, shows that just 17 (of 113) articles are joint work of researchers from different countries. Moreover, just two present joint research on game-based learning from three countries. The first one describes the methodology implemented within the European initiative Coding4girls, which proposes to teach coding through a game design based on a design thinking methodological approach linked to creativity and human-centred solutions (De Carvalho et al., 2020 ). The second joint research (Agbo et al., 2021 ) describes the students’ online co-creation of mini-games to develop their computational thinking skills. Interestingly, all other published articles describe implementing a game-based learning approach in computer science in the local context, making it difficult to generalize the conclusions and the research outcomes.

Distribution of published articles by publisher

Most of the relevant researched articles are published by IEEE Xplore (86 of 113) but mostly published as part of the proceedings at different conferences. This might explain why the number of published articles from IEEE Xplore differs from other publishing companies. Figure  4 presents the distribution of the articles by each of the publishers in detail, comparing published articles in journals and at conferences.

Distribution of the published articles by different publishers

Distribution of published articles by educational level

Identifying the number of articles according to the educational level was more complicated due to the different educational systems in different countries, resulting in a different understanding of the terms “primary”, and “secondary” education. In some countries, the same educational level is entitled as “primary”, and in others as “lower secondary” or even “middle school”. For example, in some countries, the primary school includes 6–14-year-old students; in others, it is divided, so there are primary (from 6 to 10 years), middle (11–13 years) and high schools (14–18 years); and in some, there are even lower secondary school (12–16 years). Therefore, we have tried to combine different categories according to the student’s age and to gather three levels: primary, secondary and university, according to the local context (primary education includes 6–14 years, secondary education includes 15–18 years). The situation with the distribution of the relevant articles is presented in Fig.  5 .

Distribution of the published articles in different educational levels

It can be noticed that most of the articles concern universities, although the number of articles that concern using games in computer science in primary and secondary schools is not small. It can be expected, because most of the articles refer to using games for developing programming skills, which is present mainly at the university level. However, in some countries, primary school students learn fundamental programming concepts.

Distribution of published articles by the purpose of implementation

The purpose of the research concerning game-based learning in computer science is different and mostly depends on the type of the game as well as the topic that is covered by the game. The distribution of the published articles according to the purpose of the implementation of the research is presented in Fig.  6 . However, it must be mentioned that it was difficult to distinguish the purposes of implementing the game-based approach in computer science, because the purpose was not clearly stated in the articles or there was overlapping among different categories.

Distribution of the published articles according to the purpose of the implementation

In the most articles (66 of 113), the research is done to measure students’ learning achievement or to evaluate the benefits of the game-based approach by comparing students’ knowledge and skills before and after implementing this approach. In addition, some articles are interested in students’ engagement and raising students’ interest and motivation for the learning process by implementing a game-based approach. However, just a few articles refer to using this approach for measuring students’ overall satisfaction with the whole experience (3 of 113).

Distribution of published articles by implemented pedagogical strategy and used technology

Manual analyses of the included articles gave us insight into additional aspects of implementing a game-based approach in computer science. When we talk about the game-based approach, there are two main pedagogical strategies for implementation: students can learn by playing the game, and students can learn while creating the game. The distribution of those two approaches in the published articles indicates that learning by playing games is more frequently used than learning by creating games. Only 19 of 113 relevant articles refer to the implementation of a game-based approach, where students learn during the process of game design or are involved themselves in the creation of the game. In most of the articles, students just use the created game (previously created or designed for the purpose of the research) to develop their competencies on a given topic. Regarding the technology used for the creation of the games in the published articles, it can be noticed that most of the games are web-based (although they have a mobile version, too), and there are just a few articles concerning the use of the unplugged activities as a game-based approach for learning computer science.

Distribution of published articles by covered computer science topic

Most of the articles concerning computer science topics covered during the implementation of the game-based approach refer to using to develop students’ programming skills in object-oriented programming, followed by the articles concerning block-based programming and the development of computational thinking skills. The number of articles that utilize the game-based approach in all other computer science topics is significantly smaller (in total, 14 from 113 articles). Figure  7 contains more detailed information about this distribution.

Distribution of the published articles according to the covered computer science topics

Types of educational games used for implementation of the game-based approach in computer science

Our research aims to provide information about the latest research trends concerning game-based learning in computer science education. Table 3 gives information about the implemented game, the type of the game, the computer science topic covered by the game, and the educational level, where the research concerning the game-based approach in computer science was carried out. The type of the game refers to the origin of the game creation, whether the game was already created and can be used or is created for the research by the author or by the students (they are learning during the game design process).

Detailed analysis of these relevant articles shows that different educational games are used to implement game-based learning in computer science, implementing different technologies for their design. Articles refer to using different platforms, environments or engines for creating games using different technology. In primary education, most implemented approaches include block-based environments, such as Blocky, Snap!, and Scratch. Those platforms give access to the already created game (De Carvallho et al., 2020 ; Sáiz Manzanares et al., 2020 ; Vourletsis & Politis, 2022 ) but also offer possibilities a game to be created by a teacher (Bevčič & Rugelj, 2020 ; Holenko Dlab & Hoic-Bozic, 2021 ; Wong & Jiang, 2018 ) or by the students during the learning process (Funke et al., 2017 ; Zeevaarders & Aivaloglouor, 2021 ). Even more, their use as a platform to code Arduino boards is presented in two of the articles (Sharma et al., 2019 ; Yongqiang et al., 2018 ). Block-based environments are used in the research in secondary education, too. For example, Araujo et al. ( 2018 ) measured students’ motivation for learning block-based programming by involving students in creating games in Scratch. Schatten and Schatten ( 2020 ) involve students in creating different games using CodeCombat during the CodeWeek initiative to increase their interest in programming, and Chang and Tsai ( 2018 ) are implementing an approach for learning programming in pairs while coding Kinnect with Scratch.

However, in the research articles concerning secondary education, it can be noticed that some specified games are created by the researcher (or teacher) to develop some concrete computer science skills. In these cases, the articles focus on the evaluation of the effectiveness of the game as an approach. For example, the chatbot’s serious game “PrivaCity” (Berger et al., 2019 ) is designed to raise students’ privacy awareness, as a very important topic among teenagers.

Similarly, “Capture the flag” is a game designed for learning about network security in a vocational school (Prabawa et al., 2017 ). The effectiveness of using the educational game “Degraf” in a vocational high school as supplementary material for learning graphic design subjects is measured by Elmunsyah et al. ( 2021 ). Furthermore, Hananto and Panjaburee ( 2019 ) developed the semi-puzzle game “Key and Chest” to develop algorithm thinking skills and concluded that this digital game could lead to better achievement than if the physical game is used for the same purpose. The number of games developed at the university level on a specific topic by the researchers is even more significant. However, there is still no standardized game, and the games differ among themselves depending on the topic covered by the game and the country, where the game is implemented.

Only a few games are mentioned more than once in the list of relevant articles. The implementation of “Code defenders” to enable students to learn about software testing in a fun and competitive way is researched by Clegg et al. ( 2017 ) and Fraser et al. ( 2020 ). However, the studies continue each other, presenting improvements in the game. Different block-based programming languages and online platforms such as Scratch, Snap!, and Code Combat are mentioned in several articles, too. Implementation of a game-based approach during the assessment process through the creation of quizzes in Kahoot is presented by Abidin and Zaman ( 2017 ) and Videnovik et al. ( 2018 ). Finally, several articles refer to the use of Escape room as a popular game implemented in an educational context (Giang et al., 2020 ; López-Pernas et al., 2019 , 2021 ; Seebauer et al., 2020 ; Towler et al., 2020 ). However, all these Escape room-style games are created on different platforms and cover different topics. Therefore, it can be concluded that no standardized type of game is implemented at a certain educational level or concerning a specific topic.

Further analyses were done concerning the type of the game, referring to the origin of the game: already created and just used for the research, created by the researcher for the purpose of the research or created by the students during the learning process. The distribution of the number of articles according to the type of the game in different educational levels is presented in Fig.  8 .

Distribution of the published articles according to the game designer in different educational levels

Most of the articles describe the implementation of a game-based approach when the author creates the game to test the game’s efficiency and make improvements based on the feedback received by the students. The number of games created by the author is the biggest at the university level, and the most balanced distribution of different kinds of games (created by the author, students or already created) is present in primary education. Interestingly, the most significant number of articles that concern using games created by students is in primary education. It shows that students in primary education have been the most involved in the process of game design, although they are young and have less knowledge and skills than students at other educational levels. This could be result of the fact that the articles that refer to primary education present a game’s design only in a block-based environment and using basic programming concepts. However, research articles do not refer to a standardized methodology of a framework for the creation of a game, and each game is designed individually depending on the used technology, topic and educational level.

Pedagogical strategies for implementation of the game-based approach in computer science

A detailed analysis of the pedagogical strategies for implementing a game-based approach shows that most relevant articles use games as a tool for learning the content. This trend continues in the recent period as well (Kaldarova et al., 2023 ). Hence, students play the game (already created or created by an author) to gather knowledge or develop their skills. Detail distribution of the research articles regarding pedagogical strategies for implementing a game-based approach is presented in Fig.  9 and more detailed data can be found in Table 3 . Some articles explain how students learn during the process of the creation of a game. Those are different games at different educational levels, but they all concern the process of designing a game on some platform that will develop their programming skills. Unfortunately, no article describes the process of developing students’ knowledge and skills on different computer science topics than programming while designing a game. It is a critical gap that should be considered as a topic in future research: to see whether students can learn about other computer science topics during the game creation process (while they develop their programming skills).

Distribution of the published articles according to the implemented pedagogical strategy

Computer science topics covered by game-based approach in computer science

Figure  10 gives insight into the distribution of the relevant articles concerning the computer science topic covered by the game-based approach. The topic that is mainly taught by a game-based approach at university is object-oriented programming. The situation is similar in secondary schools. Game-based approach is suitable classroom strategy for fostering higher order thinking skills, such as problem solving, group collaboration, and critical thinking, that are developed during learning object-oriented programming, which is consistent with previous research conducted by Chen et al. ( 2021 ).

Distribution of the published articles concerning the covered computer science topics

This can be expected, because the topic is complex for the students, and teachers must find different approaches and strategies to make it more understandable. In addition, in those educational levels, there is a distribution of the articles in different mentioned computer science topics (although it is not equally distributed).

However, if we analyze the topics covered by the game-based approach in primary education, it can be noticed that this approach is implemented in several topics only, mainly connected with the development of students’ computational thinking skills and fundaments of programming languages (see Table 3 for detailed overview). This trend continues in the recent years (Cheng et al., 2023 ; Mozelius & Humble, 2023 ).

Students in primary education mostly learn block-based programming languages, so it is expected that this will be the most frequent topic covered by the game-based approach. However, some articles also refer to object-oriented programming taught in upper grades. The interesting finding is that there are no articles about using educational games to learn other computer science topics, such as hardware, some applications, networks, and cybersecurity, in primary education, as there are in other educational levels. For example, there are two articles that elaborate on learning about internet safety using games in secondary education (Berger et al., 2019 ; Prabawa et al., 2017 ), and no article on game-based learning for internet safety in primary education. This lack of research articles concerning using the game-based approach for learning other topics in computer science in primary education can help identify potential future research topics.

Potential research topics concerning the game-based approach in computer science

While the lack of research articles concerning using the game-based approach for learning other topics in computer science in primary education is a good starting point for identifying potential future research topics, it is important to consider it in combination with practical constraints such are lack of knowledge, access to technology or teacher training on a specific subject. In that context, “Identifying the challenges, opportunities and solutions for integrating game-based learning methods in primary schools for specific computer science topics” can be a future research topic. It should be noted, that although some articles on specific topics can be found in the recent literature (Alam, 2022 ), there is a huge pool of topics, such are internet safety and digital citizenship that can be explored in this context.

There is an evident lack of articles on the use of game-based learning in primary and secondary schools. The findings in the existing literature that elaborate on how specific game design elements influence the learning process are minimal (Baek & Oh, 2019 ; Dos Santos et al., 2019 ; Emembolu et al., 2019 ; Kanellopoulou et al., 2021 ). These findings, combined with the finding of a limited number of articles that use existing games in the process of learning, define the potential future research topic "Assessing the role of game design elements in enhancing engagement and understanding of computer science concepts among primary and/or secondary school students". This research topic can use conceptual framework that investigates how specific elements of game design can contribute to increased engagement and improved understanding of computer science concepts in primary or/and education.

This research topic includes various specific research questions and theoretical frameworks. One possible set of research questions can investigate the specific elements of game design that can be incorporated into educational games or learning activities to enhance the learning experience. These elements may include interactive interfaces, engaging narratives, immersive environments, feedback mechanisms, competition or collaboration features, levels of difficulty, rewards, and progression systems. Different theories such are social cognitive theory (Lim et al., 2020 ) and self-determination theory (Ryan et al., 2006 ) can be used to better understand the motivational factors of different game design elements (interactivity, challenges, and rewards), and how they influence student engagement and sustain student interest and active participation in computer science learning.

All mentioned research questions can be investigated by conducting experiments, surveys, observations, or interviews to gather quantitative and qualitative data on student experiences and perceptions. Combined with data from learning outcomes, these potential findings can provide the information about overall effectiveness of using the elements of a game-based approach to learning computer science in primary schools.

Limitations

This scoping review focuses on the articles in four digital libraries, potentially leaving a significant number of articles out of the analyzing process.

Using the NLP toolkit automates searching for relevant articles. Undoubtedly, a human reader might better understand the context and better assess the relevance of an article and potentially include some articles that NLP toolkit classified as irrelevant. In addition, after the initial selection by NLP toolkit, we performed the quality assessment of the identified articles, for each of the research questions. In that way, we ensured that only relevant articles are included in the study, but it might happen that, due to the phase of selection some relevant articles were omitted from the study.

Detailed meta-analyses within the selected group of articles concerning a particular research feature can further contribute to the existing body of knowledge. Similar analyses exist, but not on learning computer science (Gui et al., 2023 ). For example, in our manuscript, we did not consider the size of the student population, existence of the control group of students, or replicability of the studies.

This scoping review discusses implementation of game-based approach in computer science by analyzing research articles in four digital libraries published between 2017 and 2021. In total, 113 research articles were analyzed concerning the educational level, where the game-based approach is implemented, the type of the game, covered computer science topic, pedagogical strategy and purpose of the implementation. The results show that the number of research articles is increasing through the years, confirming the importance of implementing a game-based approach in computer science. Most of these articles refer to the research in just one country, in the local context, making it difficult to generalize the research outcomes and conclusions on the international level.

The article presents various games using various technologies concerning several computer science topics. However, there is no standardized game or methodology that can be used for designing an educational game. Implemented game in each of the researched articles depends on the educational level, covered topic and game type. From our findings, it is evident that most articles refer to the implementation of the game-based approach, where students gather the necessary knowledge and skills while playing a game. Just a few of them incorporate the process of learning by designing educational games, and this learning is connected to developing computational thinking or programming skills.

Potential future research might be focused on identifying the challenges, opportunities, and solutions for integrating game-based learning methods for a specific computer science topic. Example topics might be internet safety and digital citizenship.

The lack of research articles on game-based learning in primary and secondary schools, along with limited findings on the influence of game design elements, highlights the need to assess how different elements enhance engagement and understanding of computer science concepts.

Availability of data and materials

All data generated and analyzed during this study are included in this article.

https://scratch.mit.edu/

https://snap.berkeley.edu/

https://blockly.games/

https://codecombat.com/

https://www.alice.org/

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Videnovik, M., Vold, T., Kiønig, L. et al. Game-based learning in computer science education: a scoping literature review. IJ STEM Ed 10 , 54 (2023). https://doi.org/10.1186/s40594-023-00447-2

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• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

• Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
• Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
• Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
• Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
• Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

Top 10 Computer Science Research Paper Topics

1. battery life and energy storage for 5g equipment.

The 5G network is an upcoming cellular network with much higher data rates and capacity than the current 4G network. According to research published in the European Scientific Institute Journal, one of the main concerns with the 5G network is the high energy consumption of the 5G-enabled devices . Hence, this research on this topic can highlight the challenges and proffer unique solutions to make more energy-efficient designs.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Other Examples of Computer Science Research Topics & Questions

Computer research topics.

• The confluence of theoretical computer science, deep learning, computational algorithms, and performance computing
• Exploring human-computer interactions and the importance of usability in operating systems
• Predicting the limits of networking and distributed systems
• Controlling data mining on public systems through third-party applications
• The impact of green computing on the environment and computational science

Computer Research Questions

• Why are there so many programming languages?
• Is there a better way to enhance human-computer interactions in computer-aided learning?
• How safe is cloud computing, and what are some ways to enhance security?
• Can computers effectively assist in the sequencing of human genes?
• How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

• Will it be possible to build artificial emotional intelligence?
• Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
• Can artificial intelligence systems self-improve with knowledge from the Internet?

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25+ Research Ideas in Computer Science for High School Students

As a high school student, you may be wondering how to take your interest in computer science to the next level. One way to do so is by pursuing a research project. By conducting research in computer science, you can deepen your understanding of this field, gain valuable skills, and make a contribution to the broader community. With more colleges going test-optional, a great research project will also help you stand out in an authentic way!

Research experience can help you develop critical thinking, problem-solving, and communication skills. These skills are valuable not only in computer science but also in many other fields. Moreover, research experience can be a valuable asset when applying to college or for scholarships, as it demonstrates your intellectual curiosity and commitment to learning.

Ambitious high school students who are selected for the Lumiere Research Scholar Programs work on a research area of their interest and receive 1-1 mentorship by top Ph.D. scholars. Below, we share some of the research ideas that have been proposed by our research mentors – we hope they inspire you!

Topic 1: Generative AI

Tools such as ChatGPT, Jasper.ai, StableDiffusion and NeuralText have taken the world by storm. But this is just one major application of what AI is capable of accomplishing. These are deep learning-based models , a field of computer science that is inspired by the structure of the human brain and tries to build systems that can learn! AI is a vast field with substantial overlaps with machine learning , with multiple intersections with disciplines such as medicine, art, and other STEM subjects. You could pick any of the following topics (as an example) on which to base your research.

1. Research on how to use AI systems to create tools that augment human skills. For example, how to use AI to create detailed templates for websites, apps, and all sorts of technical and non-technical documentation

2. Research on how to create multi-modal systems. For example, use AI to create a chatbot that can allow users Q&A capabilities on the contents of a podcast series, a television show, and a very diverse range of content.

3. Research on how to use AI to create tools that can do automated checks for quality and ease of understanding for student essays and other natural language tasks. This can help students quickly improve their writing skills by improving the feedback mechanism.

4. Develop a computer vision system to monitor wildlife populations in a specific region.

5. Investigate the use of computer vision in detecting and diagnosing medical conditions from medical images.

6. Extracting fashion trends (or insert any other observable here) from public street scene data (i.e. Google Street View, dash cam datasets, etc.)

Ideas by a Lumiere Mentor from Cornell University.

Topic 2: Data Science

As a budding computer scientist, you must have studied the importance of sound, accurate data that can be used by computer systems for multiple uses. A good example of data science used in education is tools that help calculate your chances of admission to a particular college. By collecting a small amount of data from you, and by comparing it with a much larger database that has been refined and updated regularly, these tools effectively use data science to calculate acceptance rates for students in a matter of seconds.

Another area is Natural Language Processing, or NLP, for short, aims to understand and improve machines' ability to understand and interpret human language. Be it the auto-moderation of content on Reddit, or developing more helpful, intuitive chatbots, you can pick any research idea that you're interested in.

You could pick one of the following, or related questions to study, that come under the umbrella of data science.

7. Develop a predictive model to forecast traffic congestion in your city.

8. Analyze the relationship between social media usage and mental health outcomes in a specific demographic.

9. Investigate the use of data analytics in reducing energy consumption in commercial buildings.

10. Develop a chatbot that can answer questions about a specific topic or domain, such as healthcare or sports.

11. Learn the different machine learning and natural language processing methods to categorize text (e.g. Amazon reviews) as positive or negative.

12. Investigate the use of natural language processing techniques in sentiment analysis of social media data.

Ideas by a Lumiere Mentor from the University of California, Irvine.

Topic 3: Robotics

A perfect research area if you're interested in both engineering and computer science , robotics is a vast field with multiple real-world applications. Robotics as a research area is a lot more hands-on than the other topics covered in this blog, so it's a good idea to make a note of all the possible tools, guides, time, and space that you may need for the following ideas. You can also pitch some of these ideas to your school if equipped with a robotics lab so that you can conduct your research in the safety of your school, and also receive guidance from your teachers!

13. Design and build a robot that can perform a specific task, such as picking up and stacking blocks.

14. Investigate the use of robots in medicine, such as high-precision surgical robots.

15. Develop algorithms to enable a robot to navigate and interact with an unfamiliar environment.

Ideas by a Lumiere Mentor from University College London.

Topic 4: Ethics in computer science

With the rapid development of technology, ethics has become a significant area of study. Ethical principles and moral values in computer science can relate to the design, development, use, and impact of computer systems and technology. It involves analyzing the potential ethical implications of new technologies and considering how they may affect individuals, society, and the environment. Some of the key ethical issues in computer science include privacy, security, fairness, accountability, transparency, and responsibility. If this sounds interesting, you could consider the following topics:

16. Investigate fairness in machine learning. There is growing concern about the potential for machine learning algorithms to perpetuate and amplify biases in data. Research in this area could explore ways to ensure that machine learning models are fair and do not discriminate against certain groups of people.

17. Study the energy consumption and carbon footprint of machine learning can have significant environmental impacts. Research in this area could explore ways to make machine learning more energy-efficient and environmentally sustainable.

18. Conduct Privacy Impact Assessments for a variety of tools for identifying and evaluating the privacy risks associated with a particular technology or system.

Topic 5: Game Development

According to statistics, the number of gamers worldwide is expected to hit 3.32 billion by 2024. This leaves an enormous demand for innovation and research in the field of game design, an exciting field of research. You could explore the field from multiple viewpoints, such as backend game development, analysis of various games, user targeting, as well as using AI to build and improve gaming models. If you're a gamer, or someone interested in game design, pursuing ideas like the one below can be a great starting point for your research -

19. Design and build a serious game that teaches users about a specific topic, such as renewable energy or financial literacy.

20. Analyze the impact of different game mechanics on player engagement and enjoyment.

21. Develop an AI-powered game that can adjust difficulty based on player skill level.

Topic 6: Cybersecurity

According to past research, there are over 2,200 attacks each day which breaks down to nearly 1 cyberattack every 39 seconds. In a world where digital privacy is of utmost importance, research in the field of cybersecurity deals with improving security in online platforms, spotting malware and potential attacks, and protecting databases and systems from malware and cybercrime is an excellent, relevant area of research. Here are a few ideas you could explore -

22. Investigate the use of blockchain technology in enhancing cybersecurity in a specific industry or application.

23. Apply ML to solve real-world security challenges, detect malware, and build solutions to safeguard critical infrastructure.

24. Analyze the effectiveness of different biometric authentication methods in enhancing cybersecurity.

Ideas by Lumiere Mentor from Columbia University

Topic 7: Human-Computer Interaction

Human-Computer Interaction, or HCI, is a growing field in the world of research. As a high school student, tapping into the various applications of HCI-based research can be a fruitful path for further research in college. You can delve into fields such as medicine, marketing, and even design using tools developed using concepts in HCI. Here are a few research ideas that you could pick -

25. Research the use of color in user interfaces and how it affects user experience.

26. Investigate the use of machine learning in predicting and improving user satisfaction with a specific software application.

27. Develop a system to allow individuals with mobility impairments to control computers and mobile devices using eye tracking.

28. Use tools like WAVE or WebAIM to evaluate the accessibility of different websites

Topic 8: Computer Networks

Computer networks refer to the communication channels that allow multiple computers and other devices to connect and communicate with each other. An advantage of conducting research in the field of computer networks is that these networks span from local, regional, and other small-scale networks to global networks. This gives you a great amount of flexibility while scoping out your research, enabling you to study a particular region that is accessible to you and is achievable in terms of time, resources, and complexity. Here are a few ideas -

29. Investigate the use of software-defined networking in enhancing network security and performance.

30. Develop a network traffic classification system to detect and block malicious traffic.

31. Analyze the effectiveness of different network topology designs in reducing network latency and congestion.

Topic 9: Cryptography

Cryptography is the practice of secure communication in the presence of third parties or adversaries. It uses mathematical algorithms and protocols to transform plain text into a form that is unintelligible to unauthorized users - the process known as encryption.

Cryptography has grown in uses - starting from securing communication over the internet, protecting sensitive information like passwords and financial transactions, and securing digital signatures and certificates.

32. Investigating side-channel attacks that exploit weaknesses in the physical implementation of cryptographic systems.

33. Research techniques that can enable secure and private machine learning using cryptographic methods.

Additional topics:

IoT: How can networked devices help us enrich human lives?

Computational Modeling: Using CS to model and study complex systems using math, physics, and computer science. Used for everything from weather forecasts, flight simulators, earthquake prediction, etc.

Parallel and distributed systems: Research into algorithms, operating systems and computer architectures built to operate in a highly parallelized manner and take advantage of large clusters of computing devices to perform highly specialized tasks. Used in data centers, supercomputers and by all major web-scale platforms like Amazon, Google, Facebook, etc.

UI/UX Design: Research into using design to improve all kinds of applications

Social Network Analysis: Exploring social structures through network and graph theory. Was used during COVID to make apps that can alert people about potential vectors of disease – be they places, events or people.

Optimization Techniques: optimization problems are common in all engineering disciplines, as well as AI and Machine Learning. Many of the common algorithms to solve them have been inspired by natural phenomena such as foraging behavior of ants or how birds naturally seem to be able to form large swarms that don’t crash into each other. This is a rich area of research that can help with innumerable problems across the disciplines.

Experimental Design: Research into the design and implementation of experimental procedures. Used in everything from Ai and Machine learning, to medicine, sociology, and most social and natural sciences.

Autonomous vehicle: Research into technical and non-technical aspects (user adoption, driver behavior) of self-driving cars

Augmented and Artificial Reality systems: Research into integrating AR to enhance and enrich everyday human experience. Augmenting gaming or augmented learning, for example.

Customized Hardware Research: Modern applications run on customized hardware. AI systems have their own architecture; crypto, its own. Modern systems have decoders built into your CPU, and this allows for highly compressed high quality video streams to play in real-time. Customized hardware is becoming increasingly critical for next-gen applications, from both a performance and an efficiency lens.

Database Systems: Research in the algorithms, systems, and architecture of database systems to enable effective storage, retrieval and usage of data of different types (text, image, sensor, streaming, etc) and sizes (small to petabytes)

Programming languages: Research into how computing languages translate human thought into machine code, and how the design of the language can significantly modify the kind of tools and applications that can be built in that language.

Bioinformatics and Computational Biology: Research into how computational methods can be applied to biological data such as cell populations, genetic sequences, to make predictions/discovery. Interdisciplinary field involving biology, modeling and simulation, and analytical methods.

If you're looking for a real-world internship that can help boost your resume while applying to college, we recommend Ladder Internships!

Ladder Internships  is a selective program equipping students with virtual internship experiences at startups and nonprofits around the world!  

The startups range across a variety of industries, and each student can select which field they would most love to deep dive into. This is also a great opportunity for students to explore areas they think they might be interested in, and better understand professional career opportunities in those areas.

The startups are based all across the world, with the majority being in the United States, Asia and then Europe and the UK. 

The fields include technology, machine learning and AI, finance, environmental science and sustainability, business and marketing, healthcare and medicine, media and journalism and more.

You can explore all the options here on their application form . As part of their internship, each student will work on a real-world project that is of genuine need to the startup they are working with, and present their work at the end of their internship. In addition to working closely with their manager from the startup, each intern will also work with a Ladder Coach throughout their internship - the Ladder Coach serves as a second mentor and a sounding board, guiding you through the internship and helping you navigate the startup environment. 

Cost : $1490 (Financial Aid Available)

Location:   Remote! You can work from anywhere in the world.

Application deadline:  April 16 and May 14

Program dates:  8 weeks, June to August

Eligibility: Students who can work for 10-20 hours/week, for 8-12 weeks. Open to high school students, undergraduates and gap year students!

Additionally, you can also work on independent research in AI, through Veritas AI's Fellowship Program!

Veritas AI focuses on providing high school students who are passionate about the field of AI a suitable environment to explore their interests. The programs include collaborative learning, project development, and 1-on-1 mentorship.  

These programs are designed and run by Harvard graduate students and alumni and you can expect a great, fulfilling educational experience. Students are expected to have a basic understanding of Python or are recommended to complete the AI scholars program before pursuing the fellowship. 

The   AI Fellowship  program will have students pursue their own independent AI research project. Students work on their own individual research projects over a period of 12-15 weeks and can opt to combine AI with any other field of interest. In the past, students have worked on research papers in the field of AI & medicine, AI & finance, AI & environmental science, AI & education, and more! You can find examples of previous projects   here . 

Location : Virtual

$1,790 for the 10-week AI Scholars program

$4,900 for the 12-15 week AI Fellowship 

$4,700 for both

Need-based financial aid is available. You can apply   here . 

Application deadline : On a rolling basis. Applications for fall cohort have closed September 3, 2023. 

Program dates : Various according to the cohort

Program selectivity : Moderately selective

Eligibility : Ambitious high school students located anywhere in the world. AI Fellowship applicants should either have completed the AI Scholars program or exhibit past experience with AI concepts or Python.

Application Requirements: Online application form, answers to a few questions pertaining to the students background & coding experience, math courses, and areas of interest. 

Additionally, you can check out some summer programs that offer courses in computer science such as the Lumiere Scholars Program !

Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a PhD student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.

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110+ Exceptional Education Research Topics Ideas

Topics for education research usually comprise school research topics, research problems in education, qualitative research topics in education, and concept paper topics about education to mention a few.

If you’re looking for research titles about education,  you’re reading the right post! This article contains 110 of the best education research topics that will come in handy when you need to choose one for your research. From sample research topics in education, to research titles examples for high school students about education – we have it all.

Educational Research Topics

Research title examples for college students, quantitative research titles about education, topics related to education for thesis, research titles about school issues, ph.d. research titles in education, elementary education research topics, research title examples about online class, research titles about modular learning, examples of research questions in education, special education research titles.

The best research titles about education must be done through the detailed process of exploring previous works and improving personal knowledge.

Here are some good research topics in education to consider.

What Are Good Research Topics Related to Education?

• The role of Covid-19 in reinvigorating online learning
• The growth of cognitive abilities through leisure experiences
• The merits of group study in education
• Merits and demerits of traditional learning methods
• The impact of homework on traditional and modern education
• Student underdevelopment as a result of larger class volumes
• Advantages of digital textbooks in learning
• The struggle of older generations in computer education
• The standards of learning  in the various academic levels
• Bullying and its effects on educational and mental health
• Exceptional education tutors: Is the need for higher pay justifiable?

The following examples of research titles about education for college students are ideal for a project that will take a long duration to complete. Here are some education topics for research that you can consider for your degree.

• Modern classroom difficulties of students and teachers
• Strategies to reform the learning difficulties within schools
• The rising cost of tuition and its burden on middle-class parents
• The concept of creativity among public schools and how it can be harnessed
• Major difficulties experienced in academic staff training
• Evaluating the learning cultures of college students
• Use of scientific development techniques in student learning
• Research of skill development in high school and college students
• Modern grading methods in underdeveloped institutions
• Dissertations and the difficulties surrounding their completion
• Integration of new gender categories in personalized learning

These research topics about education require a direct quantitative analysis and study of major ideas and arguments. They often contain general statistics and figures to back up regular research. Some of such research topics in education include:

• The relationship between poor education and increased academic fees
• Creating a social link between homeschool and traditional schoolgoers
• The relationship between teacher satisfaction and student performance
• The divide between public and private school performance
• The merits of parental involvement in students’ cognitive growth.
• A study on child welfare and its impact on educational development
• The relationship between academic performance and economic growth
• Urbanization in rural areas and its contribution to institutional growth
• The relationship between students and professors in dissertation writing
• The link between debt accumulation and student loans
• Boarding schools and regular schools: The role these two school types play in cognitive development

Educational-related topics used for a thesis normally require a wide aspect of study and enough educational materials.  Here are some education research topics you can use for write my thesis .

• The difficulties of bilingual education in private universities
• Homework and its impact on learning processes in college education
• Dissertation topic selection: Key aspects and research obligations
• Social media research topics and their educational functions
• A detailed educational review of student learning via virtual reality techniques
• Ethnicities in universities and their participation in group activities
• The modern approach to self-studying for college students
• Developing time management skills in modern education
• Guidelines for teacher development in advanced educational institutions
• The need for religious education in boarding schools
• A measure of cognitive development using digital learning methods

A research title about school issues focuses on activities surrounding the school environment and its effects on students, teachers, parents, and education in general. Below are some sample research titles in education, relating to school issues.

• Learning English in bilingual schools
• A study of teachers’ role as parent figures on school grounds
• Addressing the increased use of illegal substances and their effects in schools
• The benefits of after-class activities for foreign students
• Assessing student and teacher relationships
• A study of the best methods to implement safety rules in school
• Major obstacles in meeting school schedules using boarding students as a case study
• The need for counseling in public and private schools: Which is greater?
• Academic volunteering in understaffed public schools
• Modern techniques for curbing school violence among college students
• The advantages and disadvantages of teacher unions in schools

As you create your proposed list of research topics in education, consider scientific journals for referencing purposes. Here are some Ph.D. research titles for education.

• The modern methods of academic research writing
• The role of colleges in advanced mental care
• The merits and demerits of Ph.D. studies in Europe and Africa
• Interpersonal relationships between students and professors in advanced institutions
• A review of community colleges: merits and demerits
• Assessing racism in academic ethnic minorities
• The psychological changes of students in higher education
• The questionable standards of student loan provisions
• The merits of personalized teaching techniques in colleges
• The wage gap between private and public university teachers
• Teacher responsibilities in private universities versus public universities

The research topics in elementary education in 2023 are very different from the elementary education research topics from five or ten years ago. This creates interesting grounds for different research titles for elementary education.

Here are some elementary education title research ideas.

• Assessing quick computer literacy among elementary school pupils.
• The role of video games in childhood brain development
• Male vs female role models in early education periods
• The advantages of digital textbooks in elementary schools
• The impact of modern curriculums on elementary education
• Lack of proper school grooming is a cause of violence.
• Should elementary school children be taught about LGBTQ?
• A review of the need for sexual education in elementary schools
• The effects of emotional dependence in early childhood learners.
• The need for constant technology supervision of elementary school students
• Advantages of computer-guided education in elementary schools

Here are some research title examples for students taking online classes.

• The academic difficulties experienced by online students.
• A study of decreased attention in online classes
• The upsides and downsides of online education
• The rising fees of online and traditional education in universities
• A detailed study on the necessity of college internships
• The need to provide college scholarships based on environmental achievements
• How online education terminates university fraternities and sororities.
• The role of academic supervisors in career selection
• Why interactive assignments improved learning capabilities during the pandemic
• Merits of education in online learning environments
• Why online lessons are the least effective for some college students

The modular learning approach focuses primarily on learning outcomes. Here are some examples of research titles about modular learning.

• Modular learning and the role of teachers in its execution
• Teaching techniques of religious institutions
• Potential risks of accelerated learning
• Modular learning on students’ future performances
• The general overview of modular learning amongst students
• The modern Advantages and disadvantages of inclusive classes
• Observing student developments in modular learning
• Music therapy for fostering modular learning techniques
• The creation of a personalized curriculum for students.
• Applications of modular learning both in home-schooling?
• The benefits of modular learning towards creating a more holistic educational system

These research title examples about education answer important questions and they can also be argumentative essay topics .

Here are some titles of research about education questions.

• What impacts do learning approaches provide for students?
• How can schools manage their increasing gender differences?
• What fosters the provision of learning needs?
• What are the best educational recruitment methods?
• How can cognitive development improve education?
• How can you assess the moral growth of institutions?
• What are the primary causes of educational differences in geographical locations?
• How can institutions address increasing mental health needs?
• Why is early intervention essential in students with mental health setbacks?
• What are the characteristics of mental health deterioration among students?
• What techniques are acceptable in regulating the violence of students in institutions

Some of the research title examples about education include:

• How do schools create more personalized learning methods?
• Evaluating mental health setbacks during education
• The impact of modern technology on special education
• The cognitive improvements via specialized learning in dyslexic children
• The psychological link between dyslexia and bullying in high school
• Impact of social isolation in special education classes
• The difficulties in providing specialized learning environments
• A study of orphan students with disabilities and their aptitudes for learning
• How special classes improve the self-esteem of disabled students.
• How to use modern teaching techniques in unique learning environments.
• A study of the application of digital games to autistic learning

Final words about education research topics

We have provided some reliable examples of a research topic about education you can use for write my thesis . You can use these research titles in education to cultivate your ideas, create inspiration, or for online research. Remember always to select a topic that you’re naturally passionate about and do diligent research, and reach out to our professional writing services if you need any help.

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Dissertations / Theses on the topic 'Computer science in Education'

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Consult the top 50 dissertations / theses for your research on the topic 'Computer science in Education.'

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Bewley, Samantha. "High School Computer Science Education." Thesis, Villanova University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13426311.

One of the challenges in the field of computer science is teaching the subject at the high school level. Thirteen computer science teachers, one technology teacher and one department chair for technology were interviewed to determine how they thought computer science education could be improved at the high school level. The qualitative research addressed curriculum, professional development, educational computer science standards and frameworks, technology, and pedagogy. Institutional Review Board approval was obtained for the research. Nvivo was used to analyze the interviews. When the results were compiled, many teachers were concerned that there were low numbers of students interested in computer science. Having low numbers or students enrolled in computer science classes contribute to low numbers of computer science teachers. Different way to address these problems are proposed.

Ryu, Mike Dongyub. "Improving Introductory Computer Science Education with DRaCO." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1943.

Peterson, Cynthia Lynn. "Using computer technology to enhance science education." CSUSB ScholarWorks, 2002. https://scholarworks.lib.csusb.edu/etd-project/2109.

Hickey, Peter J. "A microcomputer network for computer science education." Thesis, University of Ottawa (Canada), 1986. http://hdl.handle.net/10393/5023.

Gibson, Benjamin Ian. "Educational Games for Teaching Computer Science." Thesis, University of Canterbury. Computer Science and Software Engineering, 2013. http://hdl.handle.net/10092/9239.

English, John. "A building blocks approach to computer science education." Thesis, University of Brighton, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485950.

Enström, Emma. "On difficult topics in theoretical computer science education." Doctoral thesis, KTH, Teoretisk datalogi, TCS, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-152357.

QC 20140929

Aldakheel, Eman A. "A Cloud Computing Framework for Computer Science Education." Bowling Green State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1322873621.

Prottsman, Christie Lee Lili. "Computational Thinking and Women in Computer Science." Thesis, University of Oregon, 2011. http://hdl.handle.net/1794/11485.

Mitchell, Carmen L. (Carmen Lois). "The Contributions of Grace Murray Hopper to Computer Science and Computer Education." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278692/.

Henderson, Craig Allen 1972. "RobotWorld : a simulation environment for introductory computer science education." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80075.

Hutchins-Korte, Laura. "Learning by game-building in theoretical computer science education." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3162.

Mork, Kirsten L. "Evaluating Creative Choice in K-12 Computer Science Curriculum." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2038.

Anandan, Senthil Kumar. "Animation tool kit for computer science education on the Internet." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0004/MQ45314.pdf.

Symeonidis, Pavlos. "Automated assessment of Java programming coursework for computer science education." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437093.

Schroeder, Leah M. "The value of computer-assisted instruction in secondary science education." CSUSB ScholarWorks, 1986. https://scholarworks.lib.csusb.edu/etd-project/172.

Liebe, Christine Lynn. "An Examination of Abstraction in K-12 Computer Science Education." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6728.

Nadarajah, Kumaravel. "Computers in science teaching: a reality or dream; The role of computers in effective science education: a case of using a computer to teach colour mixing; Career oriented science education for the next millennium." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1003341.

Peterson, Sarah Budinger. "Factors relating to the acquisition of computer literacy and computer science skills in California high schools." Scholarly Commons, 1986. https://scholarlycommons.pacific.edu/uop_etds/3071.

Lowhorn, Greg L., and Anthony Pittarese. "Business Literacy for the Computer Science Professional." Digital Commons @ East Tennessee State University, 2008. https://dc.etsu.edu/etsu-works/3011.

Stejskal, Ryan. "Test-Driven Learning in High School Computer Science." Thesis, University of Nebraska at Omaha, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1554641.

Test-driven development is a style of software development that emphasizes writing tests first and running them frequently with the aid of automated testing tools. This development style is widely used in the software development industry to improve the rate of development while reducing software defects. Some computer science educators are adopting the test-driven development approach to help improve student understanding and performance on programming projects. Several studies have examined the benefits of teaching test-driven programming techniques to undergraduate student programmers, with generally positive results. However, the usage of test-driven learning at the high school level has not been studied to the same extent. This thesis investigates the use of test-driven learning in high school computer science classes and whether test-driven learning provides benefits for high school as well as college students.

Alharbi, Eman. "Characterize the Difficulties that International Computer Science Students Face." Thesis, University of Colorado at Colorado Springs, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10102300.

International Computer Science students, who form the majority of students in Engineering colleges in the U.S (Anderson, 2013), face a lot of difficulties and barriers that are unknown and unexpressed. Hiding these struggles may affect the quality of their education, and will repeat the struggles over and over with the coming students. We conducted a qualitative study to discover the barriers that international Computer Science students have and their special needs. The data was collected by interviewing international Computer Science students and some of their instructors in the University of Colorado at Colorado Springs (UCCS). The study found that international Computer Science students have English barriers evaluated on the following dimensions: listening and understanding lectures, participating and expressing ideas, presenting, writing, and reading. Moreover, students have identified another set of difficulties, which is technical barriers based on educational background and the ability to deal with advanced software tools.

Ochwa-Echel, James R. "Gender gap in computer science education : experiences of women in Uganda /." View abstract, 2005. http://wwwlib.umi.com/dissertations/fullcit/3191711.

Kelkar, Shreeharsh. "Platformizing higher education : computer science and the making of MOOC infrastructures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107312.

Behnam, Humam, and Artin Mirzaian. "Evaluation of template-based programming question generation for Computer Science education." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280114.

Chiang, Yen-Hsi. "Advising module: Graduate application system for the Computer Science Graduate Program." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2725.

Turner, Scott Alexander. "minimUML: A Minimalist Approach to UML Diagraming for Early Computer Science Education." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/33030.

Acton, Donald, Kimberly Voll, Steven Wolfman, and Benjamin Yu. "Pedagogical Transformations in the UBC CS Science Education Initiative." ACM, 2009. http://hdl.handle.net/2429/8884.

Barton, Roy. "Computers and practical work in science education : a comparative study." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318020.

Jamwal, Goldee. "Effective use of Interactive Learning Modules in Classroom Study for Computer Science Education." DigitalCommons@USU, 2012. http://digitalcommons.usu.edu/etd/1358.

Rimington, Keith B. "Expanding the Horizons of Educational Pair Programming: A Methodological Review of Pair Programming in Computer Science Education Research." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/601.

Nivens, Ryan Andrew, and Rosemary Geiken. "Using a Computer Science-Based Board Game to Develop Preschoolers' Mathematics." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/214.

Bell, Richard Scott. "Low overhead methods for improving education capacity and outcomes in computer science." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18168.

Behnke, Kara Alexandra. "Gamification in Introductory Computer Science." Thesis, University of Colorado at Boulder, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3743653.

This thesis investigates the impact of gamification on student motivation and learning in several introductory computer science educational activities. The use of game design techniques in education offers the potential to make learning more motivating and more enjoyable for students. However, the design, implementation, and evaluation of game elements that actually realize this promise remains a largely unmet challenge. This research examines whether the introduction of game elements into curriculum positively impacts student motivation and intended learning outcomes for entry-level computer science education in four settings that apply similar game design techniques in different introductory computer science educational settings. The results of these studies are evaluated using mixed methods to compare the effects of game elements on student motivation and learning in both formal and non-formal learning environments.

Finch, Dylan Keifer. "Improving and Evaluating Maria: A Virtual Teaching Assistant for Computer Science Education." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98573.

Burke, Lauren. "Computer Science Education at The Claremont Colleges: The Building of an Intuition." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/scripps_theses/875.

Crosier, Joanna. "Virtual environments for science education : a schools-based development." Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323181.

Saw, Yihui. "Enlight : a projected augmented reality approach to science education." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100671.

Furby, Thomas H. "A comparison of Workforce Education/Human Resource faculty and Computer Science faculty perceptions towards distance education /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1147198101&sid=16&Fmt=2&clientId=1509&RQT=309&VName=PQD.

Hunter, Jeffrey C. "Student Engagement in a Computer Rich Science Classroom." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1426713813.

Prayaga, Lakshmi. "Game development environment to teach computer science concepts." [Pensacola, Fla.] : University of West Florida, 2007. http://purl.fcla.edu/fcla/etd/WFE0000089.

Polycarpou, Irene. "An Innovative Approach to Teaching Structural Induction for Computer Science." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/18.

Wiggberg, Mattias. "Computer Science Project Courses : Contrasting Students’ Experiences with Teachers’ Expectations." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120081.

DeVaney, Jonah E. "tidyTouch: An Interactive Visualization Tool for Data Science Education." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/honors/529.

Hewner, Michael. "Student conceptions about the field of computer science." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45890.

Bushey, Dean E. "Critical thinking traits of top-tier experts and implications for computer science education." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1193079316/.

Jakupovic, Jasmin. "Educated to Learn : How to enhance the education of computer science and informatics." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-31902.

Nivens, Ryan Andrew, and Rosemary Geiken. "Using a Computer Science-based Board Game to Develop Preschoolers' Mathematics." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/3040.

Sheldon, Daniel K. (Daniel Kenneth) 1974. "Computer assisted group decision making for education program development." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80120.

Maczka, Darren Kurtis. "Computing Trajectories: Pathways into Computer Science and Programming Experience in the First Year." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/92198.

Four of the biggest problems facing education—and four trends that could make a difference

Eduardo velez bustillo, harry a. patrinos.

In 2022, we published, Lessons for the education sector from the COVID-19 pandemic , which was a follow up to,  Four Education Trends that Countries Everywhere Should Know About , which summarized views of education experts around the world on how to handle the most pressing issues facing the education sector then. We focused on neuroscience, the role of the private sector, education technology, inequality, and pedagogy.

Unfortunately, we think the four biggest problems facing education today in developing countries are the same ones we have identified in the last decades .

1. The learning crisis was made worse by COVID-19 school closures

Low quality instruction is a major constraint and prior to COVID-19, the learning poverty rate in low- and middle-income countries was 57% (6 out of 10 children could not read and understand basic texts by age 10). More dramatic is the case of Sub-Saharan Africa with a rate even higher at 86%. Several analyses show that the impact of the pandemic on student learning was significant, leaving students in low- and middle-income countries way behind in mathematics, reading and other subjects.  Some argue that learning poverty may be close to 70% after the pandemic , with a substantial long-term negative effect in future earnings. This generation could lose around $21 trillion in future salaries, with the vulnerable students affected the most.

2. Countries are not paying enough attention to early childhood care and education (ECCE)

At the pre-school level about two-thirds of countries do not have a proper legal framework to provide free and compulsory pre-primary education. According to UNESCO, only a minority of countries, mostly high-income, were making timely progress towards SDG4 benchmarks on early childhood indicators prior to the onset of COVID-19. And remember that ECCE is not only preparation for primary school. It can be the foundation for emotional wellbeing and learning throughout life; one of the best investments a country can make.

3. There is an inadequate supply of high-quality teachers

Low quality teaching is a huge problem and getting worse in many low- and middle-income countries.  In Sub-Saharan Africa, for example, the percentage of trained teachers fell from 84% in 2000 to 69% in 2019 . In addition, in many countries teachers are formally trained and as such qualified, but do not have the minimum pedagogical training. Globally, teachers for science, technology, engineering, and mathematics (STEM) subjects are the biggest shortfalls.

4. Decision-makers are not implementing evidence-based or pro-equity policies that guarantee solid foundations

It is difficult to understand the continued focus on non-evidence-based policies when there is so much that we know now about what works. Two factors contribute to this problem. One is the short tenure that top officials have when leading education systems. Examples of countries where ministers last less than one year on average are plentiful. The second and more worrisome deals with the fact that there is little attention given to empirical evidence when designing education policies.

To help improve on these four fronts, we see four supporting trends:

1. Neuroscience should be integrated into education policies

Policies considering neuroscience can help ensure that students get proper attention early to support brain development in the first 2-3 years of life. It can also help ensure that children learn to read at the proper age so that they will be able to acquire foundational skills to learn during the primary education cycle and from there on. Inputs like micronutrients, early child stimulation for gross and fine motor skills, speech and language and playing with other children before the age of three are cost-effective ways to get proper development. Early grade reading, using the pedagogical suggestion by the Early Grade Reading Assessment model, has improved learning outcomes in many low- and middle-income countries. We now have the tools to incorporate these advances into the teaching and learning system with AI , ChatGPT , MOOCs and online tutoring.

2. Reversing learning losses at home and at school

There is a real need to address the remaining and lingering losses due to school closures because of COVID-19.  Most students living in households with incomes under the poverty line in the developing world, roughly the bottom 80% in low-income countries and the bottom 50% in middle-income countries, do not have the minimum conditions to learn at home . These students do not have access to the internet, and, often, their parents or guardians do not have the necessary schooling level or the time to help them in their learning process. Connectivity for poor households is a priority. But learning continuity also requires the presence of an adult as a facilitator—a parent, guardian, instructor, or community worker assisting the student during the learning process while schools are closed or e-learning is used.

To recover from the negative impact of the pandemic, the school system will need to develop at the student level: (i) active and reflective learning; (ii) analytical and applied skills; (iii) strong self-esteem; (iv) attitudes supportive of cooperation and solidarity; and (v) a good knowledge of the curriculum areas. At the teacher (instructor, facilitator, parent) level, the system should aim to develop a new disposition toward the role of teacher as a guide and facilitator. And finally, the system also needs to increase parental involvement in the education of their children and be active part in the solution of the children’s problems. The Escuela Nueva Learning Circles or the Pratham Teaching at the Right Level (TaRL) are models that can be used.

3. Use of evidence to improve teaching and learning

We now know more about what works at scale to address the learning crisis. To help countries improve teaching and learning and make teaching an attractive profession, based on available empirical world-wide evidence , we need to improve its status, compensation policies and career progression structures; ensure pre-service education includes a strong practicum component so teachers are well equipped to transition and perform effectively in the classroom; and provide high-quality in-service professional development to ensure they keep teaching in an effective way. We also have the tools to address learning issues cost-effectively. The returns to schooling are high and increasing post-pandemic. But we also have the cost-benefit tools to make good decisions, and these suggest that structured pedagogy, teaching according to learning levels (with and without technology use) are proven effective and cost-effective .

4. The role of the private sector

When properly regulated the private sector can be an effective education provider, and it can help address the specific needs of countries. Most of the pedagogical models that have received international recognition come from the private sector. For example, the recipients of the Yidan Prize on education development are from the non-state sector experiences (Escuela Nueva, BRAC, edX, Pratham, CAMFED and New Education Initiative). In the context of the Artificial Intelligence movement, most of the tools that will revolutionize teaching and learning come from the private sector (i.e., big data, machine learning, electronic pedagogies like OER-Open Educational Resources, MOOCs, etc.). Around the world education technology start-ups are developing AI tools that may have a good potential to help improve quality of education .

After decades asking the same questions on how to improve the education systems of countries, we, finally, are finding answers that are very promising.  Governments need to be aware of this fact.

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Consultant, Education Sector, World Bank

Senior Adviser, Education

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Computer Science: What Schools Need to Know to Teach It Today

Doug Konopelko is the Senior Manager of Education Impact at CDW.

Students can build many skills when learning computer science , beyond the ones typically taught in traditional computer science classes. They can learn pattern recognition, problem-solving through debugging, and even project management as they break down a problem into its requisite parts and test and implement solutions. All of these skills can benefit students in a wide variety of jobs, whether related directly to technology or not.

There has been buzz around computer science in K–12 education for the past 15 years, but with the rise in technology in classrooms and careers, teachers need to understand how many of these skills and opportunities translate to the real world.

Computer science no longer needs to be a stand-alone course in a student’s academic journey. It can be integrated into teaching and learning in myriad ways depending on a school’s mission and resources.

Click the banner to build the right IT environment in your school with managed services.

Integrating Computer Science into a K–12 School

There are many subject matter areas in which schools can incorporate computer science. To build a more traditional computer science program, a school might use its career and technical education program . Typically, CTE programs give students access to more powerful devices and the opportunity to participate in an IT academy or cybersecurity training.

However, computer science can also be implemented into courses where it’s a piece of the larger puzzle. For example, it could be incorporated into a class on game design or a school’s core subject areas.

Schools can do it with almost any subject depending on how deep they want to go. If, for example, there’s a component within biology that’s really heavy with data and analytics, then any research students do is going to be data and analytics driven. This could help students understand, say, how computer science is used in marine biology . The right tools and framing for the lessons can help bridge those gaps.

KEEP READING: Students are programming robots to help K–12 schools.

Some schools are taking the lessons further, having students work on simulated or real IT equipment to teach them technology skills. Some programs put students in front of enterprise-grade hardware, and schools that train their students often have them closing out real IT tickets. Some even hire their students to work for the school after they graduate.

Finding and Keeping Teachers for K–12 Computer Science Courses

One of the biggest challenges for today’s K–12 schools is finding and retaining teachers who can teach computer science. Students who are trained and subsequently employed by the district often take additional classes or certifications in their free time and end up leaving the education sector to work elsewhere.

And it can be difficult to find people with computer science training and teaching skills. To teach a high school computer science class, there’s a big difference between a chemistry teacher who has learned some programming in his or her free time and someone with 10 years of programming experience in the tech industry. Most people in technical fields tend to stay in those fields; they don’t often choose later on to work in education.

RELATED: Artificial intelligence helps teachers defeat burnout and boost productivity.

Some schools choose to add a bonus or stipend to computer science teaching positions. Other schools might work with the computer science teacher to build the program from the ground up. This creates a vested interest on the part of the educator who worked to create the program.

With the release of the Department of Education’s 2024 update to the National Educational Technology Plan and the department’s previous artificial intelligence guidance, school leaders have a lot of strong reference material in their corner when petitioning their superintendents and school board members for computer science funding . Whether they use such funding for equipment in their CTE or core subject courses or to help pay for a computer science teacher, having the money is a crucial step to getting these lessons integrated into the K–12 environment.

This article is part of the  ConnectIT: Bridging the Gap Between Education and Technology  series. Please join the discussion on Twitter by using the  #ConnectIT  hashtag.

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Past, Present and Future of Computing Education Research pp 9–31 Cite as

What is Computing Education Research (CER)?

• Mats Daniels 4 ,
• Lauri Malmi 5 ,
• Arnold Pears 6 &
• First Online: 05 January 2023

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This chapter follows the development of Computing Education Research (CER) from how the CER community emerged from investigating teaching computer science (CS) as a tertiary education subject to becoming a research discipline of its own. Given the rapid growth of Computing as a discipline and the complexity of the research foci aligned with the educational transformation, it is clear that a single definition of CER is not possible. However, taking a historical perspective, including the development of a sense of scholarship, allows us to analyze the focus of CER over time. Furthermore, we will provide an environmental structure for CER that includes the components computing in general , learning and teaching computing , and educational research , to discuss the interaction and overlap between CER and the other aspects of the field of Computing. The concept of scholarship gives a common ground for valuing CER. To that end, we provide a short introduction to scholarship based on a framework developed by Glassick et al. (Scholarship assessed: evaluation of the professoriate. Jossey-Bass, San Francisco, 1997) as a basis for the CER community. Finally, we will reflect on the status of CER as a discipline. In this, we will use some criteria from Fensham (Defining an Identity: The Evolution of Science Education as a Field of Research. Springer Science & Business Media, 2004) for a discipline and provide our assessment of how well CER fulfills these criteria. We argue that CER has matured to be seen as a legitimate research discipline and conclude by relating CER to other examples of Discipline Based Education Research (DBER). The chapter lays the groundwork for some of the remaining chapters by presenting our perspective on influential contributions to the international dialogue concerning the content and structure of CER. The chapter also provides an overview of some attempts to define the field, including significant books about CER, panel sessions at major conferences, taxonomies, and structured literature reviews.

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Universities Have a Computer-Science Problem

The case for teaching coders to speak French

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Last year, 18 percent of Stanford University seniors graduated with a degree in computer science, more than double the proportion of just a decade earlier. Over the same period at MIT, that rate went up from 23 percent to 42 percent . These increases are common everywhere: The average number of undergraduate CS majors at universities in the U.S. and Canada tripled in the decade after 2005, and it keeps growing . Students’ interest in CS is intellectual—culture moves through computation these days—but it is also professional. Young people hope to access the wealth, power, and influence of the technology sector.

That ambition has created both enormous administrative strain and a competition for prestige. At Washington University in St. Louis, where I serve on the faculty of the Computer Science & Engineering department, each semester brings another set of waitlists for enrollment in CS classes. On many campuses, students may choose to study computer science at any of several different academic outposts, strewn throughout various departments. At MIT, for example, they might get a degree in “Urban Studies and Planning With Computer Science” from the School of Architecture, or one in “Mathematics With Computer Science” from the School of Science, or they might choose from among four CS-related fields within the School of Engineering. This seepage of computing throughout the university has helped address students’ booming interest, but it also serves to bolster their demand.

Another approach has gained in popularity. Universities are consolidating the formal study of CS into a new administrative structure: the college of computing. MIT opened one in 2019. Cornell set one up in 2020. And just last year, UC Berkeley announced that its own would be that university’s first new college in more than half a century. The importance of this trend—its significance for the practice of education, and also of technology—must not be overlooked. Universities are conservative institutions, steeped in tradition. When they elevate computing to the status of a college, with departments and a budget, they are declaring it a higher-order domain of knowledge and practice, akin to law or engineering. That decision will inform a fundamental question: whether computing ought to be seen as a superfield that lords over all others, or just a servant of other domains, subordinated to their interests and control. This is, by no happenstance, also the basic question about computing in our society writ large.

When I was an undergraduate at the University of Southern California in the 1990s, students interested in computer science could choose between two different majors: one offered by the College of Letters, Arts and Sciences, and one from the School of Engineering. The two degrees were similar, but many students picked the latter because it didn’t require three semesters’ worth of study of a (human) language, such as French. I chose the former, because I like French.

An American university is organized like this, into divisions that are sometimes called colleges , and sometimes schools . These typically enjoy a good deal of independence to define their courses of study and requirements as well as research practices for their constituent disciplines. Included in this purview: whether a CS student really needs to learn French.

The positioning of computer science at USC was not uncommon at the time. The first academic departments of CS had arisen in the early 1960s, and they typically evolved in one of two ways: as an offshoot of electrical engineering (where transistors got their start), housed in a college of engineering; or as an offshoot of mathematics (where formal logic lived), housed in a college of the arts and sciences. At some universities, including USC, CS found its way into both places at once.

The contexts in which CS matured had an impact on its nature, values, and aspirations. Engineering schools are traditionally the venue for a family of professional disciplines, regulated with licensure requirements for practice. Civil engineers, mechanical engineers, nuclear engineers, and others are tasked to build infrastructure that humankind relies on, and they are expected to solve problems. The liberal-arts field of mathematics, by contrast, is concerned with theory and abstraction. The relationship between the theoretical computer scientists in mathematics and the applied ones in engineers is a little like the relationship between biologists and doctors, or physicists and bridge builders. Keeping applied and pure versions of a discipline separate allows each to focus on its expertise, but limits the degree to which one can learn from the other.

Read: Programmers, stop calling yourself engineers

By the time I arrived at USC, some universities had already started down a different path. In 1988, Carnegie Mellon University created what it says was one of the first dedicated schools of computer science. Georgia Institute of Technology followed two years later. “Computing was going to be a big deal,” says Charles Isbell, a former dean of Georgia Tech’s college of computing and now the provost at the University of Wisconsin-Madison. Emancipating the field from its prior home within the college of engineering gave it room to grow, he told me. Within a decade, Georgia Tech had used this structure to establish new research and teaching efforts in computer graphics, human-computer interaction, and robotics. (I spent 17 years on the faculty there, working for Isbell and his predecessors, and teaching computational media.)

Kavita Bala, Cornell University’s dean of computing, told me that the autonomy and scale of a college allows her to avoid jockeying for influence and resources. MIT’s computing dean, Daniel Huttenlocher, says that the speed at which computing evolves justifies the new structure.

But the computing industry isn’t just fast-moving. It’s also reckless. Technology tycoons say they need space for growth, and warn that too much oversight will stifle innovation. Yet we might all be better off, in certain ways, if their ambitions were held back even just a little. Instead of operating with a deep understanding or respect for law, policy, justice, health, or cohesion, tech firms tend to do whatever they want . Facebook sought growth at all costs, even if its take on connecting people tore society apart . If colleges of computing serve to isolate young, future tech professionals from any classrooms where they might imbibe another school’s culture and values—engineering’s studied prudence, for example, or the humanities’ focus on deliberation—this tendency might only worsen.

Read: The moral failure of computer scientists

When I raised this concern with Isbell, he said that the same reasoning could apply to any influential discipline, including medicine and business. He’s probably right, but that’s cold comfort. The mere fact that universities allow some other powerful fiefdoms to exist doesn’t make computing’s centralization less concerning. Isbell admitted that setting up colleges of computing “absolutely runs the risk” of empowering a generation of professionals who may already be disengaged from consequences to train the next one in their image. Inside a computing college, there may be fewer critics around who can slow down bad ideas. Disengagement might redouble. But he said that dedicated colleges could also have the opposite effect. A traditional CS department in a school of engineering would be populated entirely by computer scientists, while the faculty for a college of computing like the one he led at Georgia Tech might also house lawyers, ethnographers, psychologists, and even philosophers like me. Huttenlocher repeatedly emphasized that the role of the computing college is to foster collaboration between CS and other disciplines across the university. Bala told me that her college was established not to teach CS on its own but to incorporate policy, law, sociology, and other fields into its practice. “I think there are no downsides,” she said.

Mark Guzdial is a former faculty member in Georgia Tech’s computing college, and he now teaches computer science in the University of Michigan’s College of Engineering. At Michigan, CS wasn’t always housed in engineering—Guzdial says it started out inside the philosophy department, as part of the College of Literature, Science and the Arts. Now that college “wants it back,” as one administrator told Guzdial. Having been asked to start a program that teaches computing to liberal-arts students, Guzdial has a new perspective on these administrative structures. He learned that Michigan’s Computer Science and Engineering program and its faculty are “despised” by their counterparts in the humanities and social sciences. “They’re seen as arrogant, narrowly focused on machines rather than people, and unwilling to meet other programs’ needs,” he told me. “I had faculty refuse to talk to me because I was from CSE.”

In other words, there may be downsides just to placing CS within an engineering school, let alone making it an independent college. Left entirely to themselves, computer scientists can forget that computers are supposed to be tools that help people. Georgia Tech’s College of Computing worked “because the culture was always outward-looking. We sought to use computing to solve others’ problems,” Guzdial said. But that may have been a momentary success. Now, at Michigan, he is trying to rebuild computing education from scratch, for students in fields such as French and sociology. He wants them to understand it as a means of self-expression or achieving justice—and not just a way of making software, or money.

Early in my undergraduate career, I decided to abandon CS as a major. Even as an undergraduate, I already had a side job in what would become the internet industry, and computer science, as an academic field, felt theoretical and unnecessary. Reasoning that I could easily get a job as a computer professional no matter what it said on my degree, I decided to study other things while I had the chance.

I have a strong memory of processing the paperwork to drop my computer-science major in college, in favor of philosophy. I walked down a quiet, blue-tiled hallway of the engineering building. All the faculty doors were closed, although the click-click of mechanical keyboards could be heard behind many of them. I knocked on my adviser’s door; she opened it, silently signed my paperwork without inviting me in, and closed the door again. The keyboard tapping resumed.

The whole experience was a product of its time, when computer science was a field composed of oddball characters, working by themselves, and largely disconnected from what was happening in the world at large. Almost 30 years later, their projects have turned into the infrastructure of our daily lives. Want to find a job? That’s LinkedIn. Keep in touch? Gmail, or Instagram. Get news? A website like this one, we hope, but perhaps TikTok. My university uses a software service sold by a tech company to run its courses. Some things have been made easier with computing. Others have been changed to serve another end, like scaling up an online business.

Read: So much for ‘learn to code’

The struggle to figure out the best organizational structure for computing education is, in a way, a microcosm of the struggle under way in the computing sector at large. For decades, computers were tools used to accomplish tasks better and more efficiently. Then computing became the way we work and live. It became our culture, and we began doing what computers made possible, rather than using computers to solve problems defined outside their purview. Tech moguls became famous, wealthy, and powerful. So did CS academics (relatively speaking). The success of the latter—in terms of rising student enrollments, research output, and fundraising dollars—both sustains and justifies their growing influence on campus.

If computing colleges have erred, it may be in failing to exert their power with even greater zeal. For all their talk of growth and expansion within academia, the computing deans’ ambitions seem remarkably modest. Martial Hebert, the dean of Carnegie Mellon’s computing school, almost sounded like he was talking about the liberal arts when he told me that CS is “a rich tapestry of disciplines” that “goes far beyond computers and coding.” But the seven departments in his school correspond to the traditional, core aspects of computing plus computational biology. They do not include history, for example, or finance. Bala and Isbell talked about incorporating law, policy, and psychology into their programs of study, but only in the form of hiring individual professors into more traditional CS divisions. None of the deans I spoke with aspires to launch, say, a department of art within their college of computing, or one of politics, sociology, or film. Their vision does not reflect the idea that computing can or should be a superordinate realm of scholarship, on the order of the arts or engineering. Rather, they are proceeding as though it were a technical school for producing a certain variety of very well-paid professionals. A computing college deserving of the name wouldn’t just provide deeper coursework in CS and its closely adjacent fields; it would expand and reinvent other, seemingly remote disciplines for the age of computation.

Near the end of our conversation, Isbell mentioned the engineering fallacy, which he summarized like this: Someone asks you to solve a problem, and you solve it without asking if it’s a problem worth solving. I used to think computing education might be stuck in a nesting-doll version of the engineer’s fallacy, in which CS departments have been asked to train more software engineers without considering whether more software engineers are really what the world needs. Now I worry that they have a bigger problem to address: how to make computer people care about everything else as much as they care about computers.

This article originally mischaracterized the views of MIT’s computing dean, Daniel Huttenlocher. He did not say that computer science would be held back in an arts-and-science or engineering context, or that it needs to be independent.

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How People Are Really Using GenAI

• Marc Zao-Sanders

The top 100 use cases as reported by users on Reddit, Quora, and other forums.

There are many use cases for generative AI, spanning a vast number of areas of domestic and work life. Looking through thousands of comments on sites such as Reddit and Quora, the author’s team found that the use of this technology is as wide-ranging as the problems we encounter in our lives. The 100 categories they identified can be divided into six top-level themes, which give an immediate sense of what generative AI is being used for: Technical Assistance & Troubleshooting (23%), Content Creation & Editing (22%), Personal & Professional Support (17%), Learning & Education (15%), Creativity & Recreation (13%), Research, Analysis & Decision Making (10%).

It’s been a little over a year since ChatGPT brought generative AI into the mainstream. In that time, we’ve ridden a wave of excitement about the current utility and future impact of large language models (LLMs). These tools already have hundreds of millions of weekly users, analysts are projecting a multi-trillion dollar contribution to the economy, and there’s now a growing array of credible competitors to OpenAI.

• Marc Zao-Sanders is CEO and co-founder of filtered.com , which develops algorithmic technology to make sense of corporate skills and learning content. He’s the author of Timeboxing – The Power of Doing One Thing at a Time . Find Marc on LinkedIn or at www.marczaosanders.com .

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What the Data Says About Pandemic School Closures, Four Years Later

The more time students spent in remote instruction, the further they fell behind. And, experts say, extended closures did little to stop the spread of Covid.

By Sarah Mervosh ,  Claire Cain Miller and Francesca Paris

Four years ago this month, schools nationwide began to shut down, igniting one of the most polarizing and partisan debates of the pandemic.

Some schools, often in Republican-led states and rural areas, reopened by fall 2020. Others, typically in large cities and states led by Democrats, would not fully reopen for another year.

A variety of data — about children’s academic outcomes and about the spread of Covid-19 — has accumulated in the time since. Today, there is broad acknowledgment among many public health and education experts that extended school closures did not significantly stop the spread of Covid, while the academic harms for children have been large and long-lasting.

While poverty and other factors also played a role, remote learning was a key driver of academic declines during the pandemic, research shows — a finding that held true across income levels.

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic .” Score changes are measured from 2019 to 2022. In-person means a district offered traditional in-person learning, even if not all students were in-person.

“There’s fairly good consensus that, in general, as a society, we probably kept kids out of school longer than we should have,” said Dr. Sean O’Leary, a pediatric infectious disease specialist who helped write guidance for the American Academy of Pediatrics, which recommended in June 2020 that schools reopen with safety measures in place.

There were no easy decisions at the time. Officials had to weigh the risks of an emerging virus against the academic and mental health consequences of closing schools. And even schools that reopened quickly, by the fall of 2020, have seen lasting effects.

But as experts plan for the next public health emergency, whatever it may be, a growing body of research shows that pandemic school closures came at a steep cost to students.

The longer schools were closed, the more students fell behind.

At the state level, more time spent in remote or hybrid instruction in the 2020-21 school year was associated with larger drops in test scores, according to a New York Times analysis of school closure data and results from the National Assessment of Educational Progress , an authoritative exam administered to a national sample of fourth- and eighth-grade students.

At the school district level, that finding also holds, according to an analysis of test scores from third through eighth grade in thousands of U.S. districts, led by researchers at Stanford and Harvard. In districts where students spent most of the 2020-21 school year learning remotely, they fell more than half a grade behind in math on average, while in districts that spent most of the year in person they lost just over a third of a grade.

( A separate study of nearly 10,000 schools found similar results.)

Such losses can be hard to overcome, without significant interventions. The most recent test scores, from spring 2023, show that students, overall, are not caught up from their pandemic losses , with larger gaps remaining among students that lost the most ground to begin with. Students in districts that were remote or hybrid the longest — at least 90 percent of the 2020-21 school year — still had almost double the ground to make up compared with students in districts that allowed students back for most of the year.

Some time in person was better than no time.

As districts shifted toward in-person learning as the year went on, students that were offered a hybrid schedule (a few hours or days a week in person, with the rest online) did better, on average, than those in places where school was fully remote, but worse than those in places that had school fully in person.

Students in hybrid or remote learning, 2020-21

80% of students

Some schools return online, as Covid-19 cases surge. Vaccinations start for high-priority groups.

Teachers are eligible for the Covid vaccine in more than half of states.

Most districts end the year in-person or hybrid.

Source: Burbio audit of more than 1,200 school districts representing 47 percent of U.S. K-12 enrollment. Note: Learning mode was defined based on the most in-person option available to students.

Income and family background also made a big difference.

A second factor associated with academic declines during the pandemic was a community’s poverty level. Comparing districts with similar remote learning policies, poorer districts had steeper losses.

But in-person learning still mattered: Looking at districts with similar poverty levels, remote learning was associated with greater declines.

A community’s poverty rate and the length of school closures had a “roughly equal” effect on student outcomes, said Sean F. Reardon, a professor of poverty and inequality in education at Stanford, who led a district-level analysis with Thomas J. Kane, an economist at Harvard.

Score changes are measured from 2019 to 2022. Poorest and richest are the top and bottom 20% of districts by percent of students on free/reduced lunch. Mostly in-person and mostly remote are districts that offered traditional in-person learning for more than 90 percent or less than 10 percent of the 2020-21 year.

But the combination — poverty and remote learning — was particularly harmful. For each week spent remote, students in poor districts experienced steeper losses in math than peers in richer districts.

That is notable, because poor districts were also more likely to stay remote for longer .

Some of the country’s largest poor districts are in Democratic-leaning cities that took a more cautious approach to the virus. Poor areas, and Black and Hispanic communities , also suffered higher Covid death rates, making many families and teachers in those districts hesitant to return.

“We wanted to survive,” said Sarah Carpenter, the executive director of Memphis Lift, a parent advocacy group in Memphis, where schools were closed until spring 2021 .

“But I also think, man, looking back, I wish our kids could have gone back to school much quicker,” she added, citing the academic effects.

Other things were also associated with worse student outcomes, including increased anxiety and depression among adults in children’s lives, and the overall restriction of social activity in a community, according to the Stanford and Harvard research .

Even short closures had long-term consequences for children.

While being in school was on average better for academic outcomes, it wasn’t a guarantee. Some districts that opened early, like those in Cherokee County, Ga., a suburb of Atlanta, and Hanover County, Va., lost significant learning and remain behind.

At the same time, many schools are seeing more anxiety and behavioral outbursts among students. And chronic absenteeism from school has surged across demographic groups .

These are signs, experts say, that even short-term closures, and the pandemic more broadly, had lasting effects on the culture of education.

“There was almost, in the Covid era, a sense of, ‘We give up, we’re just trying to keep body and soul together,’ and I think that was corrosive to the higher expectations of schools,” said Margaret Spellings, an education secretary under President George W. Bush who is now chief executive of the Bipartisan Policy Center.

Closing schools did not appear to significantly slow Covid’s spread.

Perhaps the biggest question that hung over school reopenings: Was it safe?

That was largely unknown in the spring of 2020, when schools first shut down. But several experts said that had changed by the fall of 2020, when there were initial signs that children were less likely to become seriously ill, and growing evidence from Europe and parts of the United States that opening schools, with safety measures, did not lead to significantly more transmission.

“Infectious disease leaders have generally agreed that school closures were not an important strategy in stemming the spread of Covid,” said Dr. Jeanne Noble, who directed the Covid response at the U.C.S.F. Parnassus emergency department.

Politically, though, there remains some disagreement about when, exactly, it was safe to reopen school.

Republican governors who pushed to open schools sooner have claimed credit for their approach, while Democrats and teachers’ unions have emphasized their commitment to safety and their investment in helping students recover.

“I do believe it was the right decision,” said Jerry T. Jordan, president of the Philadelphia Federation of Teachers, which resisted returning to school in person over concerns about the availability of vaccines and poor ventilation in school buildings. Philadelphia schools waited to partially reopen until the spring of 2021 , a decision Mr. Jordan believes saved lives.

“It doesn’t matter what is going on in the building and how much people are learning if people are getting the virus and running the potential of dying,” he said.

Pandemic school closures offer lessons for the future.

Though the next health crisis may have different particulars, with different risk calculations, the consequences of closing schools are now well established, experts say.

In the future, infectious disease experts said, they hoped decisions would be guided more by epidemiological data as it emerged, taking into account the trade-offs.

“Could we have used data to better guide our decision making? Yes,” said Dr. Uzma N. Hasan, division chief of pediatric infectious diseases at RWJBarnabas Health in Livingston, N.J. “Fear should not guide our decision making.”

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the Covid-19 Pandemic. ”

The study used estimates of learning loss from the Stanford Education Data Archive . For closure lengths, the study averaged district-level estimates of time spent in remote and hybrid learning compiled by the Covid-19 School Data Hub (C.S.D.H.) and American Enterprise Institute (A.E.I.) . The A.E.I. data defines remote status by whether there was an in-person or hybrid option, even if some students chose to remain virtual. In the C.S.D.H. data set, districts are defined as remote if “all or most” students were virtual.

An earlier version of this article misstated a job description of Dr. Jeanne Noble. She directed the Covid response at the U.C.S.F. Parnassus emergency department. She did not direct the Covid response for the University of California, San Francisco health system.

How we handle corrections

Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Francesca Paris is a Times reporter working with data and graphics for The Upshot. More about Francesca Paris

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AI’s future in medicine the focus of Stanford Med LIVE event

Leaders of Stanford Medicine discuss artificial intelligence in health and medicine; its usefulness in research, education and patient care; and how to responsibly integrate the technology.

March 20, 2024 - By Hanae Armitage

Nigam Shah, Natalie Pageler, David Magnus and Sylvia Plevritis , with panel moderator Michael Pfeffer, discussed ways that artificial intelligence can improve patient care and lighten providers' workload. Dorin Greenwood

Artificial intelligence-powered health care, generative models in medical research and the ethics of broad AI integration were key topics at the March 18 Stanford Med LIVE event featuring experts from across Stanford Medicine.

Panelists at the event explored what AI is; why it’s poised to change the future; and how it can support practices in research, education and patient care. It was a precursor to the first RAISE Health Symposium coming in May and sets the table for further exploration of how this current wave of excitement, fueled by advancements in generative AI technology and access to massive amounts of data, can be applied to health care and medicine.

“Now, with an explosion in new AI capabilities, we are beginning to see the full promise of this technology — as a tool with the potential to transform patient outcomes, advance biomedical education and accelerate research,” said Lloyd Minor , MD, dean of the Stanford School of Medicine and vice president of medical affairs at Stanford University.

Minor also addressed the obligation institutions like Stanford Medicine face to deploy AI tools responsibly. In partnership with the Stanford Institute for Human-Centered Artificial Intelligence, Stanford Medicine launched the Responsible AI for Safe and Equitable Health Initiative — RAISE Health — in June 2023 to ensure AI is developed, used and evaluated in medicine following best practices and the highest ethical standards.

In recent years, Stanford Medicine has begun tapping into AI’s potential applications. “At Stanford Health Care, we already have more than 30 different technology applications that leverage AI, and we will see many more of these tools coming online in the not-too-distant future,” said David Entwistle , president and CEO at Stanford Health Care. “We’re entering an exciting era of AI innovation in health and medicine, and Stanford Medicine is uniquely poised to lead.”

But, as Stanford Medicine’s other key leader pointed out, it will be critical that AI models represent all populations fairly, equitably and without bias. “To date, AI systems in medicine have been primarily trained on data from adults, as there are special privacy considerations for the use and availability of pediatric patient data,” said Paul King , president and CEO of Stanford Medicine Children’s Health. “We are actively solving this challenge at Stanford Medicine so that even our youngest patients can benefit from the same technology advances, while maintaining the necessary robust protections.”

The panel discussion, moderated by Michael Pfeffer , MD, chief information officer for Stanford Health Care and the School of Medicine, featured four speakers from Stanford Medicine:

• David Magnus , PhD, professor of medicine, biomedical ethics and pediatrics and the Thomas A. Raffin Professor in Medicine and Biomedical Ethics
• Natalie Pageler , MD, chief medical information officer at Stanford Medicine Children’s Health and clinical professor of pediatrics and medicine
• Sylvia Plevritis , PhD, chair of biomedical data science and professor of radiology
• Nigam Shah , PhD, chief data scientist at Stanford Health Care, professor of medicine and associate dean for research

AI is having a moment

Simply put, Shah told the audience, AI is the application of data by an algorithm that performs a task on behalf of, or in assistance to, a human being. The use of AI has exploded as generative AI models, such as ChatGPT — which can assimilate existing data and information and apply it in a human-like fashion — have grabbed the world’s attention.

The panelists discussed how to harness that promise, honing the broader hullabaloo into something mission-driven, impact-focused and ethical. At Stanford Medicine, that implementation is surfacing in a variety of ways, from helping kids manage Type 1 diabetes, to solving challenges in data scarcity, to creating new drugs and therapeutics with higher efficiency and lower toxicity. Outside of research, Pfeffer also pointed to two uses that are poised to enhance clerical practices for clinicians: ambient listening tools that generate clinical notes for doctors and large language models that draft responses to patient messages.

As panelists shared sentiments of anticipation and excitement, all emphasized human-centric, responsible integration of AI. “There’s so much more to providing care than just what AI can provide,” Pageler said. “It’s important that we all learn to use it, but not to be worried about being replaced.”

Deploying AI in health care

The panelists acknowledged that AI’s success in health and medicine will largely depend on the thoughtfulness and fairness with which algorithms are folded into practice.

Algorithms are not inherently neutral, Magnus said. If the data is biased, the algorithm will be too. “AI is often just a mirror. Data reflects social determinants of health; it can reflect biases in physician behavior,” he said. “That can be a problem because the models that learn from that data can either reify those biases, or we can turn them around to combat the problems that already exist.”

The AI experts say it’s crucial to look at the downstream effects of adopting AI into something as complex as a health care system. That means seeking guidance from like-minded entities such as the Coalition for Health AI and tools such as the FURM (fair, useful, reliable model) assessment, a system spearheaded by Shah and others who seek to determine whether AI tools provide fair, useful and reliable model guided care. “The point is to look at the ripple effects of using a model,” Shah said, “to think beyond the model and look at the workflow impact on real people, like workforce, patients, IT staff or nursing staff.”

These are big challenges for those aiming to get AI right. Nonetheless, the Stanford Medicine panelists shared an optimism for the future they are helping craft — largely because of where they get to do it. “Not only do we have a fantastic medical center, but we have an entire university that’s within walking distance, and we connect every day with our colleagues from medicine, engineering, humanities and other specialties,” Plevritis said. “I feel like we’re on the precipice of new knowledge, and we’re truly on the best campus to see it through.”

For more news about responsible AI in health and medicine, sign up for the RAISE Health newsletter.

Register for the RAISE Health Symposium on May 14.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Artificial intelligence

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