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Physics: Writing a Literature Review

Literature reviews.

A  literature review  surveys scholarly articles, books and other sources (e.g. dissertations, conference proceedings) relevant to a particular issue, area of research, or theory, providing a description, summary, and critical evaluation of each work. 

• Provide context for a research paper
• Explore the history and development of a topic
• Examine the scholarly conversation surrounding the topic
• Shows relationships between studies
• Examines gaps in research on the topic

Components 

Similar to primary research, development of the literature review requires four stages:

• Problem formulation—which topic or field is being examined and what are its component issues?
• Literature search—finding materials relevant to the subject being explored
• Data evaluation—determining which literature makes a significant contribution to the understanding of the topic
• Analysis and interpretation—discussing the findings and conclusions of pertinent literature

Conducting a Literature Review

1. choose a topic. define your research questions..

Your literature review should be guided by a central research question.  Remember, it is not a collection of loosely related studies in a field but instead represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor.

2. Decide on the scope of your review. 

• How many studies do you need to look at?
• How comprehensive should it be?
• How many years should it cover? 

Tip: This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.  

Make a list of the databases you will search.  

Where to find databases:

• Find Databases by Subject
• T he Find Articles tab of this guide

This page contains a list of the most relevant databases for most Physics research. 

4. Conduct your searches and find the literature. Keep track of your searches! 

• Review the abstracts of research studies carefully. This will save you time.
• Write down the searches you conduct in each database so that you may duplicate them if you need to later (or avoid dead-end searches   that you'd forgotten you'd already tried).
• Use the bibliographies and references of research studies you find to locate others.
• Ask your professor or a librarian if you are missing any key works in the field.

5. Review the Literature 

Some questions to help you analyze the research: 

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions. Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited?; if so, how has it been analyzed?

Tips: 

• Again, review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.

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Literature Review Basics

• Literature Review Step-by-Step
• Common Questions about Literature Reviews
• How do I craft a basic citation?
• What is citation tracing?
• How do I use Zotero for citation management?
• Who do I contact for help?

This video will provide a short introduction to literature reviews.

Steps For Writing a Literature Review

Recommended steps for writing a literature review:

• Review what a literature review is, and is not 
• Review your assignment and seek clarification from your instructor if needed
• Narrow your topic
• Search and gather literature resources. 
• Read and analyze literature resources
• Write the literature review
• Review appropriate  Citation and Documentation Style  for your assignment and literature review

Common Questions

What is a literature review?

A literature review is a type of scholarly, researched writing that discusses the already published information on a narrow topic . 

What is the purpose of a writing literature review?

Writing a literature review improves your personal understanding of a topic, and demonstrates your knowledge and ability to make connections between concepts and ideas. The literature review is a service to your reader, summarizing past ideas about a topic, bringing them up to date on the latest research, and making sure they have all any background information they need to understand the topic.  

What is "the literature"?

This already published information- called the literature- can be from primary information sources such as speeches, interviews, and reports, or from secondary information sources such as peer-reviewed journal articles, dissertations, and books. These type of sources are probably familiar to you from previous research projects you’ve done in your classes.

Is a literature review it's own paper?

You can write a literature review as a standalone paper , or as part of a larger research paper . When a standalone paper, the literature review acts as a summary, or snapshot, of what has been said and done about a topic in the field so far. When part of the a larger paper, a literature review still acts as a snapshot, but the prior information it provides can also support the new information, research, or arguments presented later in the paper.

Does a literature review contain an argument?

No, a literature review does NOT present an argument or new information. The literature review is a foundation that summarizes and synthesizes the existing literature in order for you and your readers to understand what has already been said and done about your topic.

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Doing a literature review using digital tools (with Notion template)

I’ve recently revamped my literature review workflow since discovering Notion . Notion is an organization application that allows you to make various pages and databases. It’s kind of like your own personal wiki- you can link your pages and embed databases into another page, adding filters and sorting them using user-set properties. The databases are what I use the most. I’ve essentially transferred all of my excel sheets into Notion databases and find it much easier to filter and sort things now. In this post, I’ll go through how I do my literature review and share a Notion template that you can use.

I like to organize my literature review using various literature review tools along with two relational Notion databases: a ‘literature tracker’ and a ‘literature notes’ matrix. You can see a flow chart of my literature review process below (it’s inspired by this post by Jenn’s Studious Life and the three pass method for reading papers which I wrote about last week in this post ):

As you can see, this process involves a couple of decision points which helps me focus on the most important papers. This is an iterative process that keeps me up to date on relevant research in my field as I am getting new paper alerts in my inbox most days. I used this method quite successfully to write the literature review for my confirmation report and regularly add to it for the expanded version that will become part of my PhD thesis. In this post, I’ll break down how this works for me and how I implement my Notion databases to synthesise the literature I read into a coherent argument.

You can click on the links below to navigate to a particular section of this article:

The literature search

The literature tracker, the literature synthesis matrix, writing your literature review, iterating your literature review, my literature review notion template, some useful resources.

This is always the first step in building your literature review. There are plenty of resources online all about how to start with your search- I find a mixture of database search tools works for me.

The first thing to do when starting your literature review is to identify some keywords to use in your initial searches. It might be worth chatting to your supervisor to make a list of these and then add or remove terms to it as you go down different research routes. You can use keyword searches relevant to your research questions as well tools that find ‘similar’ papers and look at citation links. I also find that just looking through the bibliographies of literature in your field and seeing which papers are regularly cited gives you a good idea of the core papers in your area (you’ll start recognising the key ones after a while). Another method for finding literature is the snowballing method which is particularly useful for conducting a systematic review.

Here are some digital tools I use to help me find literature relevant to my research questions:

Library building and suggestions

Mendeley was my research management tool of choice prior to when I started using Notion to organize all of my literature and create my synthesis matrix. I still use Mendeley as a library just in case anything happens to my Notion. It’s easy to add new papers to your library using the browser extension with just one click. I like that Mendeley allows you to share your folders with colleagues and that I can export bib.tex files straight from my library into overleaf documents where I’m writing up papers and my thesis. You do need to make sure that all of the details are correct before you export the bib.tex files though as this is taken straight from the information plane. I also like to use the tag function in Mendeley to add more specific identifiers than my folders.

Mendeley is also useful for finding literature related to those in your library- I’ve found quite a few interesting papers through the email updates they send out each week with ‘suggested papers’. You can also browse these suggestions from within Mendeley and use its interface to do initial keyword searches. The key is to just scan the titles and then decide whether it’s worth your time reading the abstract and then the rest of it. It’s easy to get overwhelmed by the sheer amount of papers being published every day so being picky in what you read is important (and something I need to work on more!).

Some similar tools that allow you to build a library and get literature recommendations include Zotero , Researcher , Academia , and ResearchGate . It’s up to you which one you use for your own purposes. One big factor for me when choosing Mendeley was that my supervisor and colleagues use it so it makes it much easier to share libraries with them, so maybe ask your colleagues what they use before settling on one.

Literature databases and keyword alerts

There are a variety of databases out there for finding literature. My go-to is Web of Science as it shows you citation data and has a nice interface. I used this to begin my initial literature search using my keywords.

The other thing you can do with these kinds of tools is set up email alerts to get a list of recent work that has just been published with any keywords you set. These alerts are usually where I find papers to read during journal club with my supervisor. You can customize these emails to what suits you- mine are set to the top 10 most relevant new papers for each keyword weekly and I track around 5 words/phrases. This allows me to stay on top of the most recent literature in my field- I have alerts set up on a variety of services to ensure that I don’t miss anything crucial (and alerts from the ArXiv mean I see preprints too). Again, you need to be picky about what you read from these to ensure that they are very relevant to your research. At this stage, it’s important to spend as little time as possible scanning titles as this can easily become a time suck.

Some of the other tools I have keyword (and author) email alerts set up on are: Scopus , Google Scholar , Dimensions , and ArXiv alerts . I set 10 minutes maximum aside per day to scan through any new email alerts and save anything relevant to me into my literature tracker (which I’ll come to more later).

Literature mapping tools

There are loads of these kinds of tools out there. Literature mapping can be helpful for finding what the seminal papers are in your field and seeing how literature connects. It’s like a huge web and I find these visual interfaces make it much easier to get my head around the relationships between papers. I use two of these tools during the literature search phase of the flowchart: Citation Gecko and Connected Papers .

Citation Gecko builds you a citation tree using ‘seed papers’. You can import these from various reference management software (like Mendeley), bib.tex files or manually search for papers. This is particularly useful if your supervisor has provided you with some core papers to start off with, or you can use the key papers you identified through scanning the bibliographies of literature you read. My project is split into fairly clear ‘subprojects’ so these tools help me see connections between the various things I’m working on (or a lack of them which is good in some ways as it shows I’ve found a clear research gap!).

You can switch between different views and add connecting papers as new seed papers to expand your network. I use this tool from time to time with various different papers associated with my subprojects. It’s helped me make sure I haven’t missed any key papers when doing my literature review and I’ve found it to be fairly accurate, although sometimes more recent papers don’t have any citation data on it so that’s something to bear in mind.

Connected Papers uses a ‘similarity’ algorithm to show paper relationships. This isn’t a citation tree like Citation Gecko but it does also give you prior and derivative works if you want to look at them. All you do is put one of your key papers into the search box and ‘build a graph’. It will then show you related papers, including those which don’t have direct citation links to the key paper. I think this is great for ensuring that you’re not staying inside an insular bubble of the people who all cite each other. It also allows me to see some of the research which is perhaps a bit more tangential to my project and get an overview of where my work sits within the field more broadly.

I like Connected Paper’s key for the generated tree and that it shows where related papers connect between themselves. Again, it’s helpful for ensuring that you haven’t missed a really important work when compiling your literature review and doesn’t just rely on citation links between papers.

This is where I record the details of any paper I come across that I think might be relevant to my PhD. In some ways, it’s very similar to Mendeley but it’s a version that sits within Notion so I have some more customised filtering categories set up, like my ‘status’ field where I track which pass I am on.

Here’s what my literature tracker looks like:

The beauty of Notion is that you can decide which properties you want to record in your database and customize it to your needs. You can sort and filter using these properties including making nested filters and using multiple filters at once. This makes it really easy to find what you’re looking for. For example, say I’m doing my literature review for my ‘FIB etching’ subproject and want to see all of the papers that I marked as relevant to my PhD but haven’t started reading yet. All I need to do is add a couple of filters:

And it filters everything so that I’m just looking at the papers I want to check out. It’s this flexibility that I think really gives Notion the edge when it comes to my literature review process.

The other thing I really like about using Notion rather than excel is that I can add different database views. I especially like using the kanban board view to see where I’m at with my reading workflow:

When I add something to the literature tracker database, I scan the abstract for keywords to add and categorize it in terms of relevant topics. It’s essentially the first pass of the paper, so that involves reading the title, abstract, introduction, section headings, conclusions, and checking the references for anything you recognise. After this is done, I decide whether it’s relevant enough to my PhD to proceed to do a second pass of the paper, at which point I will progress to populating my literature notes database.

Once I’ve decided that I want to do a second pass on a paper, I then add it to the ‘literature notes’ database. This is part of the beauty of Notion: relational databases. I have ‘rollup’ properties set in the literature notes database which shows all of the things I added during my first pass and allows me to filter the matrix using them. You can watch the video below to see exactly how to add a new paper to the ‘notes’ database from the ‘tracker’ database:

During the second pass, I populate the new fields in the ‘notes’ database. These are:

Summary | Objective of study | Key Results | Theory | Materials | Methods | Conclusions | Future work suggested | Critiques | Key connected papers.

I also have various themes/questions/ideas as properties which I add a few notes on for each relevant paper. I then complete my ‘questions for critical engagement’ which are on the entry’s ‘Notes’ page and are stored in the ‘Article Template’. If you want to read more about this process, check out my ‘how to read a scientific paper’ post .

By, doing this I create a synthesis matrix where I can see a breakdown of the key aspects of each paper and can scan down a column to get an overview of all of the papers I have read. For example, if I wanted to see all of the papers about Quantum Point Contacts to get an idea of what previous work has been done so that I can identify my research gap, I can filter using the tag property and can then see the notes I wrote for each entry, broken down by section. I also have tags for my research questions or themes, materials used, experimental techniques, fabrication techniques, and anything else that comes to mind really! The more tags I have for a paper, the easier it is to filter when I want to find a specific thing.

The other property I have included in the literature notes database is ‘Key connected papers’. This is a relation but is within the database itself. So it means that I can link to the page of other papers in the literature matrix. I’ve found this to be useful for connecting to what I call ‘core’ papers. I can also filter using this property, allowing me to see my notes on all of the papers I’ve read that are related to a certain ‘core’ paper. This helps with synthesising all of the information and forming my argument.

For those papers most relevant to my research (the ‘core’ papers) I’ll also do a third pass which involves reimplementing the paper in my own words. This is quite a time-consuming task so not many papers reach this stage, but those which I have done a third pass on are the ones I know really well. My hope is that this will stand me in good stead for my viva. This process also helps me refine my research questions further as I gain a deeper understanding of the field.

I find that writing up a review is extremely intimidating, but having the literature matrix makes this process that bit easier. I won’t go into too many details as there are already loads of resources out there going into the details of writing up a review, but here’s a brief overview of my own process:

Identify your research themes

Using your literature matrix, review each research theme or question and decide which ones you are going to focus on. These will form the different sections of your literature review and help you write your thesis statement(s). You can also think about how your questions link to ensure that you’re telling a coherent story with your review.

Choose and summarize literature related to each theme

For each section, gather up the most important related literature and summarize the key points of each source. A good literature review doesn’t need to cover all the literature out there, just the most significant sources. I try to stick to around 10 or fewer key sources per section.

Critical evaluation of sources

This is where you utilize the ‘questions for critical engagement’. Make sure you evaluate the strengths and weaknesses of the studies you’re writing about. By doing this, you can establish where our knowledge is lacking which will come in helpful later when establishing a research gap.

Analyse each source in relation to other literature

Try to make sure that you are telling a coherent story by linking between your sources. You can go back to the literature matrix here and use it to group similar studies to compare and contrast them. You should also discuss the relevance of the source’s findings in relation to the broader field and core papers.

Situate your research in a research gap

This is where you justify your own research. Using what you have laid out in the rest of the review, show that there is a research gap that you plan to fill and explain how you are going to do that. This should mean that your thesis flows nicely into the next section where you’ll cover the materials and methods you used in your research project.

In some ways, a literature review never really ends. As you can see in the flowchart at the beginning of this post, I regularly update and revise my literature review as well as refining my research questions. At this point in my PhD, I think that most of my research questions are quite well defined, so I’m mostly just adding any newly published work into my review. I don’t spend much time reading literature at the moment but I’m sure I’ll return to it more regularly when I’m in the write-up phase of my PhD. There is a balance to be had between reading and writing for your literature review and actually getting on with your own research!

Here’s the link to my Notion Literature Review Template . You can duplicate it and adapt it however you want, but this should save you some time setting up the initial databases if you’d like to use my method for organizing your own literature review.

Here are some resources on how to do a literature review that I’ve found useful during my PhD:

• The Literature Review: Step-by-Step Guide for Students
• 3 Steps to Save You From Drowning in Your Literature Review
• How to write a literature review
• How to become a literature searching ninja
• Mind the gap
• 7 Secrets to Write a PhD Literature Review The Right Way

If you like my work, I’d love your support!

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11 thoughts on “Doing a literature review using digital tools (with Notion template)”

Thank you so much for your insight and structured process. This will help me a lot kicking off my Master Thesis.

The perfect method to organize the literature that I have read and will read in the future. I am so glad to have found your website, this will save me from thrashing around in the swamp of literature. I was already feeling the limits of my memory when I was doing my master thesis and this will be so helpful during my PhD.

Thank you so much for this detailed post! Lily 🙂

Thank you very much for this. I’m doing my undergrad atm and reading a lot of papers. This seems like an excellent way of tracking everything.

Thank you, you made my beginning less stressful. I like your system and i helped me a lot. I have one question (more might come later), What do you mean by " journal club with my supervisor."

This piece is really really helpful! I started from this one and went through the rest blog writings. I agree on many points with Daisy. I had an unhappy experience of PhD two years ago and now just started a new one in another country. I will take it as an adventure and enjoy it.

This is an AMAZING template. I've found this so helpful for my own workflow. Thank you so much!

I found this post really helpful. Thank you.

thank you very much!

Hi! Thank you very much for posting this guide and sharing your notion template! I do have a question—do you manually enter the references into Notion, or is there any way to speed up the process? Ta x

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Physics and Astronomy: Literature Review

• Find Articles
• Literature Review
• Evaluate Sources
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Choosing a Topic

Choosing your topic is one of the most important steps for a graduate student, and should be done in consultation with your faculty advisor.  Some of the tips presented in the video below can help you get started.

The Literature Review

This tutorial from NCSU gives a good overview of the process of the literature review.

Types of Literature Reviews

Completing Literature Reviews

Links to Further Help You...

• UNC Writing Center Handout for Writing a Lit Review
• Purdue Online Writing Lab Social Work Literature Review Guidelines (Not only for Social Work!)
• UW-Madison Writing Center Learn How to Write a Review of Literature
• University of Toronto The Literature Review: A Few Tips on Conducting It
• Mindmap The Literature Review in Under 5 Minutes
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Physics: Systematic Reviews

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• Systematic Reviews

What Is a Systematic Review?

Regular literature reviews are simply summaries of the literature on a particular topic. A systematic review, however, is a comprehensive literature review conducted to answer a specific research question. Authors of a systematic review aim to find, code, appraise, and synthesize all of the previous research on their question in an unbiased and well-documented manner. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) outline the minimum amount of information that needs to be reported at the conclusion of a systematic review project. 

Other types of what are known as "evidence syntheses," such as scoping, rapid, and integrative reviews, have varying methodologies. While systematic reviews originated with and continue to be a popular publication type in medicine and other health sciences fields, more and more researchers in other disciplines are choosing to conduct evidence syntheses. 

This guide will walk you through the major steps of a systematic review and point you to key resources including Covidence, a systematic review project management tool. For help with systematic reviews and other major literature review projects, please send us an email at  [email protected] .

Getting Help with Reviews

Organization such as the Institute of Medicine recommend that you consult a librarian when conducting a systematic review. Librarians at the University of Nevada, Reno can help you:

• Understand best practices for conducting systematic reviews and other evidence syntheses in your discipline
• Choose and formulate a research question
• Decide which review type (e.g., systematic, scoping, rapid, etc.) is the best fit for your project
• Determine what to include and where to register a systematic review protocol
• Select search terms and develop a search strategy
• Identify databases and platforms to search
• Find the full text of articles and other sources
• Become familiar with free citation management (e.g., EndNote, Zotero)
• Get access to you and help using Covidence, a systematic review project management tool

Doing a Systematic Review

• Plan - This is the project planning stage. You and your team will need to develop a good research question, determine the type of review you will conduct (systematic, scoping, rapid, etc.), and establish the inclusion and exclusion criteria (e.g., you're only going to look at studies that use a certain methodology). All of this information needs to be included in your protocol. You'll also need to ensure that the project is viable - has someone already done a systematic review on this topic? Do some searches and check the various protocol registries to find out. 
• Identify - Next, a comprehensive search of the literature is undertaken to ensure all studies that meet the predetermined criteria are identified. Each research question is different, so the number and types of databases you'll search - as well as other online publication venues - will vary. Some standards and guidelines specify that certain databases (e.g., MEDLINE, EMBASE) should be searched regardless. Your subject librarian can help you select appropriate databases to search and develop search strings for each of those databases.  
• Evaluate - In this step, retrieved articles are screened and sorted using the predetermined inclusion and exclusion criteria. The risk of bias for each included study is also assessed around this time. It's best if you import search results into a citation management tool (see below) to clean up the citations and remove any duplicates. You can then use a tool like Rayyan (see below) to screen the results. You should begin by screening titles and abstracts only, and then you'll examine the full text of any remaining articles. Each study should be reviewed by a minimum of two people on the project team. 
• Collect - Each included study is coded and the quantitative or qualitative data contained in these studies is then synthesized. You'll have to either find or develop a coding strategy or form that meets your needs. 
• Explain - The synthesized results are articulated and contextualized. What do the results mean? How have they answered your research question?
• Summarize - The final report provides a complete description of the methods and results in a clear, transparent fashion. 

Adapted from

Types of reviews, systematic review.

These types of studies employ a systematic method to analyze and synthesize the results of numerous studies. "Systematic" in this case means following a strict set of steps - as outlined by entities like PRISMA and the Institute of Medicine - so as to make the review more reproducible and less biased. Consistent, thorough documentation is also key. Reviews of this type are not meant to be conducted by an individual but rather a (small) team of researchers. Systematic reviews are widely used in the health sciences, often to find a generalized conclusion from multiple evidence-based studies. 

Meta-Analysis

A systematic method that uses statistics to analyze the data from numerous studies. The researchers combine the data from studies with similar data types and analyze them as a single, expanded dataset. Meta-analyses are a type of systematic review.

Scoping Review

A scoping review employs the systematic review methodology to explore a broader topic or question rather than a specific and answerable one, as is generally the case with a systematic review. Authors of these types of reviews seek to collect and categorize the existing literature so as to identify any gaps.

Rapid Review

Rapid reviews are systematic reviews conducted under a time constraint. Researchers make use of workarounds to complete the review quickly (e.g., only looking at English-language publications), which can lead to a less thorough and more biased review. 

Narrative Review

A traditional literature review that summarizes and synthesizes the findings of numerous original research articles. The purpose and scope of narrative literature reviews vary widely and do not follow a set protocol. Most literature reviews are narrative reviews. 

Umbrella Review

Umbrella reviews are, essentially, systematic reviews of systematic reviews. These compile evidence from multiple review studies into one usable document. 

Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal , vol. 26, no. 2, 2009, pp. 91-108. doi: 10.1111/j.1471-1842.2009.00848.x .

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Effect of integrating physics education technology simulations on students’ conceptual understanding in physics: A review of literature

Herbert james banda and joseph nzabahimana, phys. rev. phys. educ. res. 17 , 023108 – published 21 december 2021.

• Citing Articles (21)
• INTRODUCTION
• METHODOLOGY
• RESULTS AND DISCUSSION
• ACKNOWLEDGMENTS

This paper describes a comprehensive review of 31 quasi or experimental research studies from the past decade on the effect of PhET simulations on students’ conceptual understanding of physics. Two questions guided the review: (i) To what extent do PhET simulations enhance students’ conceptual understanding of physics? (ii) What are the best ways to use PhET simulations to enhance conceptual understanding of physics? The reviewed literature provides robust evidence that PhET simulations can significantly enhance students’ conceptual understanding of physics and can be integrated into many active learning instructional environments. The paper also points out gaps and directions of future research and suggests that educators integrate PhET simulations in physics to create more meaningful learning.

• Received 30 June 2021
• Accepted 20 October 2021

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.17.023108

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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• 1 African Centre of Excellence for Innovative Teaching and Learning Mathematics and Science (ACEITLMS), University of Rwanda-College of Education (UR-CE), Rukara campus P.O Box 55, Rwamagana, Kayonza, Rwanda
• 2 University of Rwanda-College of Education (UR-CE), Rukara campus P.O Box 55, Rwamagana, Kayonza, Rwanda
• * [email protected]

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Article Contents

Note to readers, funding statement, review of particle physics.

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Particle Data Group, R L Workman, V D Burkert, V Crede, E Klempt, U Thoma, L Tiator, K Agashe, G Aielli, B C Allanach, C Amsler, M Antonelli, E C Aschenauer, D M Asner, H Baer, Sw Banerjee, R M Barnett, L Baudis, C W Bauer, J J Beatty, V I Belousov, J Beringer, A Bettini, O Biebel, K M Black, E Blucher, R Bonventre, V V Bryzgalov, O Buchmuller, M A Bychkov, R N Cahn, M Carena, A Ceccucci, A Cerri, R Sekhar Chivukula, G Cowan, K Cranmer, O Cremonesi, G D'Ambrosio, T Damour, D de Florian, A de Gouvêa, T DeGrand, P de Jong, S Demers, B A Dobrescu, M D'Onofrio, M Doser, H K Dreiner, P Eerola, U Egede, S Eidelman, A X El-Khadra, J Ellis, S C Eno, J Erler, V V Ezhela, W Fetscher, B D Fields, A Freitas, H Gallagher, Y Gershtein, T Gherghetta, M C Gonzalez-Garcia, M Goodman, C Grab, A V Gritsan, C Grojean, D E Groom, M Grünewald, A Gurtu, T Gutsche, H E Haber, Matthieu Hamel, C Hanhart, S Hashimoto, Y Hayato, A Hebecker, S Heinemeyer, J J Hernández-Rey, K Hikasa, J Hisano, A Höcker, J Holder, L Hsu, J Huston, T Hyodo, Al Ianni, M Kado, M Karliner, U F Katz, M Kenzie, V A Khoze, S R Klein, F Krauss, M Kreps, P Križan, B Krusche, Y Kwon, O Lahav, J Laiho, L P Lellouch, J Lesgourgues, A R Liddle, Z Ligeti, C-J Lin, C Lippmann, T M Liss, L Littenberg, C Lourenço, K S Lugovsky, S B Lugovsky, A Lusiani, Y Makida, F Maltoni, T Mannel, A V Manohar, W J Marciano, A Masoni, J Matthews, U-G Meißner, I-A Melzer-Pellmann, M Mikhasenko, D J Miller, D Milstead, R E Mitchell, K Mönig, P Molaro, F Moortgat, M Moskovic, K Nakamura, M Narain, P Nason, S Navas, A Nelles, M Neubert, P Nevski, Y Nir, K A Olive, C Patrignani, J A Peacock, V A Petrov, E Pianori, A Pich, A Piepke, F Pietropaolo, A Pomarol, S Pordes, S Profumo, A Quadt, K Rabbertz, J Rademacker, G Raffelt, M Ramsey-Musolf, B N Ratcliff, P Richardson, A Ringwald, D J Robinson, S Roesler, S Rolli, A Romaniouk, L J Rosenberg, J L Rosner, G Rybka, M G Ryskin, R A Ryutin, Y Sakai, S Sarkar, F Sauli, O Schneider, S Schönert, K Scholberg, A J Schwartz, J Schwiening, D Scott, F Sefkow, U Seljak, V Sharma, S R Sharpe, V Shiltsev, G Signorelli, M Silari, F Simon, T Sjöstrand, P Skands, T Skwarnicki, G F Smoot, A Soffer, M S Sozzi, S Spanier, C Spiering, A Stahl, S L Stone, Y Sumino, M J Syphers, F Takahashi, M Tanabashi, J Tanaka, M Taševský, K Terao, K Terashi, J Terning, R S Thorne, M Titov, N P Tkachenko, D R Tovey, K Trabelsi, P Urquijo, G Valencia, R Van de Water, N Varelas, G Venanzoni, L Verde, I Vivarelli, P Vogel, W Vogelsang, V Vorobyev, S P Wakely, W Walkowiak, C W Walter, D Wands, D H Weinberg, E J Weinberg, N Wermes, M White, L R Wiencke, S Willocq, C G Wohl, C L Woody, W-M Yao, M Yokoyama, R Yoshida, G Zanderighi, G P Zeller, O V Zenin, R-Y Zhu, Shi-Lin Zhu, F Zimmermann, P A Zyla, Review of Particle Physics, Progress of Theoretical and Experimental Physics , Volume 2022, Issue 8, August 2022, 083C01, https://doi.org/10.1093/ptep/ptac097

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The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances.

The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings.

The complete Review (both volumes) is published online on the website of the Particle Data Group ( pdg.lbl.gov ) and in a journal. Volume 1 is available in print as the PDG Book . A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.

In the Supplementary Data section below, links for downloading individual sections of the Review have been provided for readers’ convenience.

The 2022 edition of the Review of Particle Physics should be cited as:

R.L. Workman et al. (Particle Data Group), Prog. Theor. Exp. Phys. 2022 , 083C01 (2022)

DOI: 10.1093/ptep/ptac097

For the online version see: https://pdg.lbl.gov/

The publication of the Review of Particle Physics is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE–AC02–05CH11231; by an implementing arrangement between the governments of Japan (MEXT: Ministry of Education, Culture, Sports, Science and Technology) and the United States (DOE) on cooperative research and development; by the Italian National Institute of Nuclear Physics (INFN); by the Physical Society of Japan (JPS); and by the European Laboratory for Particle Physics (CERN). Individual collaborators receive support for their PDG activities from their respective institutes or funding agencies.

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Quantum Physics

Title: systematic literature review: quantum machine learning and its applications.

Abstract: Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processing. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, there are scientific challenges that are impossible to perform by classical computation due to computational complexity or the time the calculation would take, and quantum computation is one of the possible answers. However, current quantum devices have not yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. Many articles try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.

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Physics education research for 21 st century learning

• Lei Bao   ORCID: orcid.org/0000-0003-3348-4198 1 &
• Kathleen Koenig 2  

Disciplinary and Interdisciplinary Science Education Research volume  1 , Article number:  2 ( 2019 ) Cite this article

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Education goals have evolved to emphasize student acquisition of the knowledge and attributes necessary to successfully contribute to the workforce and global economy of the twenty-first Century. The new education standards emphasize higher end skills including reasoning, creativity, and open problem solving. Although there is substantial research evidence and consensus around identifying essential twenty-first Century skills, there is a lack of research that focuses on how the related subskills interact and develop over time. This paper provides a brief review of physics education research as a means for providing a context towards future work in promoting deep learning and fostering abilities in high-end reasoning. Through a synthesis of the literature around twenty-first Century skills and physics education, a set of concretely defined education and research goals are suggested for future research, along with how these may impact the next generation physics courses and how physics should be taught in the future.

Introduction

Education is the primary service offered by society to prepare its future generation workforce. The goals of education should therefore meet the demands of the changing world. The concept of learner-centered, active learning has broad, growing support in the research literature as an empirically validated teaching practice that best promotes learning for modern day students (Freeman et al., 2014 ). It stems out of the constructivist view of learning, which emphasizes that it is the learner who needs to actively construct knowledge and the teacher should assume the role of a facilitator rather than the source of knowledge. As implied by the constructivist view, learner-centered education usually emphasizes active-engagement and inquiry style teaching-learning methods, in which the learners can effectively construct their understanding under the guidance of instruction. The learner-centered education also requires educators and researchers to focus their efforts on the learners’ needs, not only to deliver effective teaching-learning approaches, but also to continuously align instructional practices to the education goals of the times. The goals of introductory college courses in science, technology, engineering, and mathematics (STEM) disciplines have constantly evolved from some notion of weed-out courses that emphasize content drilling, to the current constructivist active-engagement type of learning that promotes interest in STEM careers and fosters high-end cognitive abilities.

Following the conceptually defined framework of twenty-first Century teaching and learning, this paper aims to provide contextualized operational definitions of the goals for twenty-first Century learning in physics (and STEM in general) as well as the rationale for the importance of these outcomes for current students. Aligning to the twenty-first Century learning goals, research in physics education is briefly reviewed to provide a context towards future work in promoting deep learning and fostering abilities in high-end reasoning in parallel. Through a synthesis of the literature around twenty-first Century skills and physics education, a set of concretely defined education and research goals are suggested for future research. These goals include: domain-specific research in physics learning; fostering scientific reasoning abilities that are transferable across the STEM disciplines; and dissemination of research-validated curriculum and approaches to teaching and learning. Although this review has a focus on physics education research (PER), it is beneficial to expand the perspective to view physics education in the broader context of STEM learning. Therefore, much of the discussion will blend PER with STEM education as a continuum body of work on teaching and learning.

Education goals for twenty-first century learning

Education goals have evolved to emphasize student acquisition of essential “21 st Century skills”, which define the knowledge and attributes necessary to successfully contribute to the workforce and global economy of the 21st Century (National Research Council, 2011 , 2012a ). In general, these standards seek to transition from emphasizing content-based drilling and memorization towards fostering higher-end skills including reasoning, creativity, and open problem solving (United States Chamber of Commerce, 2017 ). Initiatives on advancing twenty-first Century education focus on skills that converge on three broad clusters: cognitive, interpersonal, and intrapersonal, all of which include a rich set of sub-dimensions.

Within the cognitive domain, multiple competencies have been proposed, including deep learning, non-routine problem solving, systems thinking, critical thinking, computational and information literacy, reasoning and argumentation, and innovation (National Research Council, 2012b ; National Science and Technology Council, 2018 ). Interpersonal skills are those necessary for relating to others, including the ability to work creatively and collaboratively as well as communicate clearly. Intrapersonal skills, on the other hand, reside within the individual and include metacognitive thinking, adaptability, and self-management. These involve the ability to adjust one’s strategy or approach along with the ability to work towards important goals without significant distraction, both essential for sustained success in long-term problem solving and career development.

Although many descriptions exist for what qualifies as twenty-first Century skills, student abilities in scientific reasoning and critical thinking are the most commonly noted and widely studied. They are highly connected with the other cognitive skills of problem solving, decision making, and creative thinking (Bailin, 1996 ; Facione, 1990 ; Fisher, 2001 ; Lipman, 2003 ; Marzano et al., 1988 ), and have been important educational goals since the 1980s (Binkley et al., 2010 ; NCET, 1987 ). As a result, they play a foundational role in defining, assessing, and developing twenty-first Century skills.

The literature for critical thinking is extensive (Bangert-Drowns & Bankert, 1990 ; Facione, 1990 ; Glaser, 1941 ). Various definitions exist with common underlying principles. Broadly defined, critical thinking is the application of the cognitive skills and strategies that aim for and support evidence-based decision making. It is the thinking involved in solving problems, formulating inferences, calculating likelihoods, and making decisions (Halpern, 1999 ). It is the “reasonable reflective thinking focused on deciding what to believe or do” (Ennis, 1993 ). Critical thinking is recognized as a way to understand and evaluate subject matter; producing reliable knowledge and improving thinking itself (Paul, 1990 ; Siegel, 1988 ).

The notion of scientific reasoning is often used to label the set of skills that support critical thinking, problem solving, and creativity in STEM. Broadly defined, scientific reasoning includes the thinking and reasoning skills involved in inquiry, experimentation, evidence evaluation, inference and argument that support the formation and modification of concepts and theories about the natural world; such as the ability to systematically explore a problem, formulate and test hypotheses, manipulate and isolate variables, and observe and evaluate consequences (Bao et al., 2009 ; Zimmerman, 2000 ). Critical thinking and scientific reasoning share many features, where both emphasize evidence-based decision making in multivariable causal conditions. Critical thinking can be promoted through the development of scientific reasoning, which includes student ability to reach a reliable conclusion after identifying a question, formulating hypotheses, gathering relevant data, and logically testing and evaluating the hypothesis. In this way, scientific reasoning can be viewed as a scientific domain instantiation of critical thinking in the context of STEM learning.

In STEM learning, cognitive aspects of the twenty-first Century skills aim to develop reasoning skills, critical thinking skills, and deep understanding, all of which allow students to develop well connected expert-like knowledge structures and engage in meaningful scientific inquiry and problem solving. Within physics education, a core component of STEM education, the learning of conceptual understanding and problem solving remains a current emphasis. However, the fast-changing work environment and technology-driven world require a new set of core knowledge, skills, and habits of mind to solve complex interdisciplinary problems, gather and evaluate evidence, and make sense of information from a variety of sources (Tanenbaum, 2016 ). The education goals in physics are transitioning towards ability fostering as well as extension and integration with other STEM disciplines. Although curriculum that supports these goals is limited, there are a number of attempts, particularly in developing active learning classrooms and inquiry-based laboratory activities, which have demonstrated success. Some of these are described later in this paper as they provide a foundation for future work in physics education.

Interpersonal skills, such as communication and collaboration, are also essential for twenty-first Century problem-solving tasks, which are often open-ended, complex, and team-based. As the world becomes more connected in a multitude of dimensions, tackling significant problems involving complex systems often goes beyond the individual and requires working with others who are increasingly from culturally diverse backgrounds. Due to the rise of communication technologies, being able to articulate thoughts and ideas in a variety of formats and contexts is crucial, as well as the ability to effectively listen or observe to decipher meaning. Interpersonal skills can be promoted by integrating group-learning experiences into the classroom setting, while providing students with the opportunity to engage in open-ended tasks with a team of peer learners who may propose more than one plausible solution. These experiences should be designed such that students must work collaboratively and responsibly in teams to develop creative solutions, which are later disseminated through informative presentations and clearly written scientific reports. Although educational settings in general have moved to providing students with more and more opportunities for collaborative learning, a lack of effective assessments for these important skills has been a limiting factor for producing informative research and widespread implementation. See Liu ( 2010 ) for an overview of measurement instruments reported in the research literature.

Intrapersonal skills are based on the individual and include the ability to manage one’s behavior and emotions to achieve goals. These are especially important for adapting in the fast-evolving collaborative modern work environment and for learning new tasks to solve increasingly challenging interdisciplinary problems, both of which require intellectual openness, work ethic, initiative, and metacognition, to name a few. These skills can be promoted using instruction which, for example, includes metacognitive learning strategies, provides opportunities to make choices and set goals for learning, and explicitly connects to everyday life events. However, like interpersonal skills, the availability of relevant assessments challenges advancement in this area. In this review, the vast amount of studies on interpersonal and intrapersonal skills will not be discussed in order to keep the main focus on the cognitive side of skills and reasoning.

The purpose behind discussing twenty-first Century skills is that this set of skills provides important guidance for establishing essential education goals for modern society and learners. However, although there is substantial research evidence and consensus around identifying necessary twenty-first Century skills, there is a lack of research that focuses on how the related subskills interact and develop over time (Reimers & Chung, 2016 ), with much of the existing research residing in academic literature that is focused on psychology rather than education systems (National Research Council, 2012a ). Therefore, a major and challenging task for discipline-based education researchers and educators is to operationally define discipline-specific goals that align with the twenty-first Century skills for each of the STEM fields. In the following sections, this paper will provide a limited vision of the research endeavors in physics education that can translate the past and current success into sustained impact for twenty-first Century teaching and learning.

Proposed education and research goals

Physics education research (PER) is often considered an early pioneer in discipline-based education research (National Research Council, 2012c ), with well-established, broad, and influential outcomes (e.g., Hake, 1998 ; Hsu, Brewe, Foster, & Harper, 2004 ; McDermott & Redish, 1999 ; Meltzer & Thornton, 2012 ). Through the integration of twenty-first Century skills with the PER literature, a set of broadly defined education and research goals is proposed for future PER work:

Discipline-specific deep learning: Cognitive and education research involving physics learning has established a rich literature on student learning behaviors along with a number of frameworks. Some of the popular frameworks include conceptual understanding and concept change, problem solving, knowledge structure, deep learning, and knowledge integration. Aligned with twenty-first Century skills, future research in physics learning should aim to integrate the multiple areas of existing work, such that they help students develop well integrated knowledge structures in order to achieve deep leaning in physics.

Fostering scientific reasoning for transfer across STEM disciplines: The broad literature in physics learning and scientific reasoning can provide a solid foundation to further develop effective physics education approaches, such as active engagement instruction and inquiry labs, specifically targeting scientific inquiry abilities and reasoning skills. Since scientific reasoning is a more domain-general cognitive ability, success in physics can also more readily inform research and education practices in other STEM fields.

Research, development, assessment, and dissemination of effective education approaches: Developing and maintaining a supportive infrastructure of education research and implementation has always been a challenge, not only in physics but in all STEM areas. The twenty-first Century education requires researchers and instructors across STEM to work together as an extended community in order to construct a sustainable integrated STEM education environment. Through this new infrastructure, effective team-based inquiry learning and meaningful assessment can be delivered to help students develop a comprehensive skills set including deep understanding and scientific reasoning, as well as communication and other non-cognitive abilities.

The suggested research will generate understanding and resources to support education practices that meet the requirements of the Next Generation Science Standards (NGSS), which explicitly emphasize three areas of learning including disciplinary core ideas, crosscutting concepts, and practices (National Research Council, 2012b ). The first goal for promoting deep learning of disciplinary knowledge corresponds well to the NGSS emphasis on disciplinary core ideas, which play a central role in helping students develop well integrated knowledge structures to achieve deep understanding. The second goal on fostering transferable scientific reasoning skills supports the NGSS emphasis on crosscutting concepts and practices. Scientific reasoning skills are crosscutting cognitive abilities that are essential to the development of domain-general concepts and modeling strategies. In addition, the development of scientific reasoning requires inquiry-based learning and practices. Therefore, research on scientific reasoning can produce a valuable knowledge base on education means that are effective for developing crosscutting concepts and promoting meaningful practices in STEM. The third research goal addresses the challenge in the assessment of high-end skills and the dissemination of effective educational approaches, which supports all NGSS initiatives to ensure sustainable development and lasting impact. The following sections will discuss the research literature that provides the foundation for these three research goals and identify the specific challenges that will need to be addressed in future work.

Promoting deep learning in physics education

Physics education for the twenty-first Century aims to foster high-end reasoning skills and promote deep conceptual understanding. However, many traditional education systems place strong emphasis on only problem solving with the expectation that students obtain deep conceptual understanding through repetitive problem-solving practices, which often doesn’t occur (Alonso, 1992 ). This focus on problem solving has been shown to have limitations as a number of studies have revealed disconnections between learning conceptual understanding and problem-solving skills (Chiu, 2001 ; Chiu, Guo, & Treagust, 2007 ; Hoellwarth, Moelter, & Knight, 2005 ; Kim & Pak, 2002 ; Nakhleh, 1993 ; Nakhleh & Mitchell, 1993 ; Nurrenbern & Pickering, 1987 ; Stamovlasis, Tsaparlis, Kamilatos, Papaoikonomou, & Zarotiadou, 2005 ). In fact, drilling in problem solving may actually promote memorization of context-specific solutions with minimal generalization rather than transitioning students from novices to experts.

Towards conceptual understanding and learning, many models and definitions have been established to study and describe student conceptual knowledge states and development. For example, students coming into a physics classroom often hold deeply rooted, stable understandings that differ from expert conceptions. These are commonly referred to as misconceptions or alternative conceptions (Clement, 1982 ; Duit & Treagust, 2003 ; Dykstra Jr, Boyle, & Monarch, 1992 ; Halloun & Hestenes, 1985a , 1985b ). Such students’ conceptions are context dependent and exist as disconnected knowledge fragments, which are strongly situated within specific contexts (Bao & Redish, 2001 , 2006 ; Minstrell, 1992 ).

In modeling students’ knowledge structures, DiSessa’s proposed phenomenological primitives (p-prim) describe a learner’s implicit thinking, cued from specific contexts, as an underpinning cognitive construct for a learner’s expressed conception (DiSessa, 1993 ; Smith III, DiSessa, & Roschelle, 1994 ). Facets, on the other hand, map between the implicit p-prim and concrete statements of beliefs and are developed as discrete and independent units of thought, knowledge, or strategies used by individuals to address specific situations (Minstrell, 1992 ). Ontological categories, defined by Chi, describe student reasoning in the most general sense. Chi believed that these are distinct, stable, and constraining, and that a core reason behind novices’ difficulties in physics is that they think of physics within the category of matter instead of processes (Chi, 1992 ; Chi & Slotta, 1993 ; Chi, Slotta, & De Leeuw, 1994 ; Slotta, Chi, & Joram, 1995 ). More details on conceptual learning and problem solving are well summarized in the literature (Hsu et al., 2004 ; McDermott & Redish, 1999 ), from which a common theme emerges from the models and definitions. That is, learning is context dependent and students with poor conceptual understanding typically have locally connected knowledge structures with isolated conceptual constructs that are unable to establish similarities and contrasts between contexts.

Additionally, this idea of fragmentation is demonstrated through many studies on student problem solving in physics and other fields. It has been shown that a student’s knowledge organization is a key aspect for distinguishing experts from novices (Bagno, Eylon, & Ganiel, 2000 ; Chi, Feltovich, & Glaser, 1981 ; De Jong & Ferguson-Hesler, 1986 ; Eylon & Reif, 1984 ; Ferguson-Hesler & De Jong, 1990 ; Heller & Reif, 1984 ; Larkin, McDermott, Simon, & Simon, 1980 ; Smith, 1992 ; Veldhuis, 1990 ; Wexler, 1982 ). Expert’s knowledge is organized around core principles of physics, which are applied to guide problem solving and develop connections between different domains as well as new, unfamiliar situations (Brown, 1989 ; Perkins & Salomon, 1989 ; Salomon & Perkins, 1989 ). Novices, on the other hand, lack a well-organized knowledge structure and often solve problems by relying on surface features that are directly mapped to certain problem-solving outcomes through memorization (Chi, Bassok, Lewis, Reimann, & Glaser, 1989 ; Hardiman, Dufresne, & Mestre, 1989 ; Schoenfeld & Herrmann, 1982 ).

This lack of organization creates many difficulties in the comprehension of basic concepts and in solving complex problems. This leads to the common complaint that students’ knowledge of physics is reduced to formulas and vague labels of the concepts, which are unable to substantively contribute to meaningful reasoning processes. A novice’s fragmented knowledge structure severely limits the learner’s conceptual understanding. In essence, these students are able to memorize how to approach a problem given specific information but lack the understanding of the underlying concept of the approach, limiting their ability to apply this approach to a novel situation. In order to achieve expert-like understanding, a student’s knowledge structure must integrate all of the fragmented ideas around the core principle to form a coherent and fully connected conceptual framework.

Towards a more general theoretical consideration, students’ alternative conceptions and fragmentation in knowledge structures can be viewed through both the “naïve theory” framework (e.g., Posner, Strike, Hewson, & Gertzog, 1982 ; Vosniadou, Vamvakoussi, & Skopeliti, 2008 ) and the “knowledge in pieces” (DiSessa, 1993 ) perspective. The “naïve theory” framework considers students entering the classroom with stable and coherent ideas (naïve theories) about the natural world that differ from those presented by experts. In the “knowledge in pieces” perspective, student knowledge is constructed in real-time and incorporates context features with the p-prims to form the observed conceptual expressions. Although there exists an ongoing debate between these two views (Kalman & Lattery, 2018 ), it is more productive to focus on their instructional implications for promoting meaningful conceptual change in students’ knowledge structures.

In the process of learning, students may enter the classroom with a range of initial states depending on the population and content. For topics with well-established empirical experiences, students often have developed their own ideas and understanding, while on topics without prior exposure, students may create their initial understanding in real-time based on related prior knowledge and given contextual features (Bao & Redish, 2006 ). These initial states of understanding, regardless of their origin, are usually different from those of experts. Therefore, the main function of teaching and learning is to guide students to modify their initial understanding towards the experts’ views. Although students’ initial understanding may exist as a body of coherent ideas within limited contexts, as students start to change their knowledge structures throughout the learning process, they may evolve into a wide range of transitional states with varying levels of knowledge integration and coherence. The discussion in this brief review on students’ knowledge structures regarding fragmentation and integration are primarily focused on the transitional stages emerged through learning.

The corresponding instructional goal is then to help students more effectively develop an integrated knowledge structure so as to achieve a deep conceptual understanding. From an educator’s perspective, Bloom’s taxonomy of education objectives establishes a hierarchy of six levels of cognitive skills based on their specificity and complexity: Remember (lowest and most specific), Understand, Apply, Analyze, Evaluate, and Create (highest and most general and complex) (Anderson et al., 2001 ; Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956 ). This hierarchy of skills exemplifies the transition of a learner’s cognitive development from a fragmented and contextually situated knowledge structure (novice with low level cognitive skills) to a well-integrated and globally networked expert-like structure (with high level cognitive skills).

As a student’s learning progresses from lower to higher cognitive levels, the student’s knowledge structure becomes more integrated and is easier to transfer across contexts (less context specific). For example, beginning stage students may only be able to memorize and perform limited applications of the features of certain contexts and their conditional variations, with which the students were specifically taught. This leads to the establishment of a locally connected knowledge construct. When a student’s learning progresses from the level of Remember to Understand, the student begins to develop connections among some of the fragmented pieces to form a more fully connected network linking a larger set of contexts, thus advancing into a higher level of understanding. These connections and the ability to transfer between different situations form the basis of deep conceptual understanding. This growth of connections leads to a more complete and integrated cognitive structure, which can be mapped to a higher level on Bloom’s taxonomy. This occurs when students are able to relate a larger number of different contextual and conditional aspects of a concept for analyzing and evaluating to a wider variety of problem situations.

Promoting the growth of connections would appear to aid in student learning. Exactly which teaching methods best facilitate this are dependent on the concepts and skills being learned and should be determined through research. However, it has been well recognized that traditional instruction often fails to help students obtain expert-like conceptual understanding, with many misconceptions still existing after instruction, indicating weak integration within a student’s knowledge structure (McKeachie, 1986 ).

Recognizing the failures of traditional teaching, various research-informed teaching methods have been developed to enhance student conceptual learning along with diagnostic tests, which aim to measure the existence of misconceptions. Most advances in teaching methods focus on the inclusion of inquiry-based interactive-engagement elements in lecture, recitations, and labs. In physics education, these methods were popularized after Hake’s landmark study demonstrated the effectiveness of interactive-engagement over traditional lectures (Hake, 1998 ). Some of these methods include the use of peer instruction (Mazur, 1997 ), personal response systems (e.g., Reay, Bao, Li, Warnakulasooriya, & Baugh, 2005 ), studio-style instruction (Beichner et al., 2007 ), and inquiry-based learning (Etkina & Van Heuvelen, 2001 ; Laws, 2004 ; McDermott, 1996 ; Thornton & Sokoloff, 1998 ). The key approach of these methods aims to improve student learning by carefully targeting deficits in student knowledge and actively encouraging students to explore and discuss. Rather than rote memorization, these approaches help promote generalization and deeper conceptual understanding by building connections between knowledge elements.

Based on the literature, including Bloom’s taxonomy and the new education standards that emphasize twenty-first Century skills, a common focus on teaching and learning can be identified. This focus emphasizes helping students develop connections among fragmented segments of their knowledge pieces and is aligned with the knowledge integration perspective, which focuses on helping students develop and refine their knowledge structure toward a more coherently organized and extensively connected network of ideas (Lee, Liu, & Linn, 2011 ; Linn, 2005 ; Nordine, Krajcik, & Fortus, 2011 ; Shen, Liu, & Chang, 2017 ). For meaningful learning to occur, new concepts must be integrated into a learner’s existing knowledge structure by linking the new knowledge to already understood concepts.

Forming an integrated knowledge structure is therefore essential to achieving deep learning, not only in physics but also in all STEM fields. However, defining what connections must occur at different stages of learning, as well as understanding the instructional methods necessary for effectively developing such connections within each STEM disciplinary context, are necessary for current and future research. Together these will provide the much needed foundational knowledge base to guide the development of the next generation of curriculum and classroom environment designed around twenty-first Century learning.

Developing scientific reasoning with inquiry labs

Scientific reasoning is part of the widely emphasized cognitive strand of twenty-first Century skills. Through development of scientific reasoning skills, students’ critical thinking, open-ended problem-solving abilities, and decision-making skills can be improved. In this way, targeting scientific reasoning as a curricular objective is aligned with the goals emphasized in twenty-first Century education. Also, there is a growing body of research on the importance of student development of scientific reasoning, which have been found to positively correlate with course achievement (Cavallo, Rozman, Blickenstaff, & Walker, 2003 ; Johnson & Lawson, 1998 ), improvement on concept tests (Coletta & Phillips, 2005 ; She & Liao, 2010 ), engagement in higher levels of problem solving (Cracolice, Deming, & Ehlert, 2008 ; Fabby & Koenig, 2013 ); and success on transfer (Ates & Cataloglu, 2007 ; Jensen & Lawson, 2011 ).

Unfortunately, research has shown that college students are lacking in scientific reasoning. Lawson ( 1992 ) found that ~ 50% of intro biology students are not capable of applying scientific reasoning in learning, including the ability to develop hypotheses, control variables, and design experiments; all necessary for meaningful scientific inquiry. Research has also found that traditional courses do not significantly develop these abilities, with pre-to-post-test gains of 1%–2%, while inquiry-based courses have gains around 7% (Koenig, Schen, & Bao, 2012 ; Koenig, Schen, Edwards, & Bao, 2012 ). Others found that undergraduates have difficulty developing evidence-based decisions and differentiating between and linking evidence with claims (Kuhn, 1992 ; Shaw, 1996 ; Zeineddin & Abd-El-Khalick, 2010 ). A large scale international study suggested that learning of physics content knowledge with traditional teaching practices does not improve students’ scientific reasoning skills (Bao et al., 2009 ).

Aligned to twenty-first Century learning, it is important to implement curriculum that is specifically designed for developing scientific reasoning abilities within current education settings. Although traditional lectures may continue for decades due to infrastructure constraints, a unique opportunity can be found in the lab curriculum, which may be more readily transformed to include hands-on minds-on group learning activities that are ideal for developing students’ abilities in scientific inquiry and reasoning.

For well over a century, the laboratory has held a distinctive role in student learning (Meltzer & Otero, 2015 ). However, many existing labs, which haven’t changed much since the late 1980s, have received criticism for their outdated cookbook style that lacks effectiveness in developing high-end skills. In addition, labs have been primarily used as a means for verifying the physical principles presented in lecture, and unfortunately, Hofstein and Lunetta ( 1982 ) found in an early review of the literature that research was unable to demonstrate the impact of the lab on student content learning.

About this same time, a shift towards a constructivist view of learning gained popularity and influenced lab curriculum development towards engaging students in the process of constructing knowledge through science inquiry. Curricula, such as Physics by Inquiry (McDermott, 1996 ), Real-Time Physics (Sokoloff, Thornton, & Laws, 2011 ), and Workshop Physics (Laws, 2004 ), were developed with a primary focus on engaging students in cognitive conflict to address misconceptions. Although these approaches have been shown to be highly successful in improving deep learning of physics concepts (McDermott & Redish, 1999 ), the emphasis on conceptual learning does not sufficiently impact the domain general scientific reasoning skills necessitated in the goals of twenty-first Century learning.

Reform in science education, both in terms of targeted content and skills, along with the emergence of knowledge regarding human cognition and learning (Bransford, Brown, & Cocking, 2000 ), have generated renewed interest in the potential of inquiry-based lab settings for skill development. In these types of hands-on minds-on learning, students apply the methods and procedures of science inquiry to investigate phenomena and construct scientific claims, solve problems, and communicate outcomes, which holds promise for developing both conceptual understanding and scientific reasoning skills in parallel (Trowbridge, Bybee, & Powell, 2000 ). In addition, the availability of technology to enhance inquiry-based learning has seen exponential growth, along with the emergence of more appropriate research methodologies to support research on student learning.

Although inquiry-based labs hold promise for developing students’ high-end reasoning, analytic, and scientific inquiry abilities, these educational endeavors have not become widespread, with many existing physics laboratory courses still viewed merely as a place to illustrate the physical principles from the lecture course (Meltzer & Otero, 2015 ). Developing scientific ideas from practical experiences, however, is a complex process. Students need sufficient time and opportunity for interaction and reflection on complex, investigative tasks. Blended learning, which merges lecture and lab (such as studio style courses), addresses this issue to some extent, but has experienced limited adoption, likely due to the demanding infrastructure resources, including dedicated technology-intensive classroom space, equipment and maintenance costs, and fully committed trained staff.

Therefore, there is an immediate need to transform the existing standalone lab courses, within the constraints of the existing education infrastructure, into more inquiry-based designs, with one of its primary goals dedicated to developing scientific reasoning skills. These labs should center on constructing knowledge, along with hands-on minds-on practical skills and scientific reasoning, to support modeling a problem, designing and implementing experiments, analyzing and interpreting data, drawing and evaluating conclusions, and effective communication. In particular, training on scientific reasoning needs to be explicitly addressed in the lab curriculum, which should contain components specifically targeting a set of operationally-defined scientific reasoning skills, such as ability to control variables or engage in multivariate causal reasoning. Although effective inquiry may also implicitly develop some aspects of scientific reasoning skills, such development is far less efficient and varies with context when the primary focus is on conceptual learning.

Several recent efforts to enhance the standalone lab course have shown promise in supporting education goals that better align with twenty-first Century learning. For example, the Investigative Science Learning Environment (ISLE) labs involve a series of tasks designed to help students develop the “habits of mind” of scientists and engineers (Etkina et al., 2006 ). The curriculum targets reasoning as well as the lab learning outcomes published by the American Association of Physics Teachers (Kozminski et al., 2014 ). Operationally, ISLE methods focus on scaffolding students’ developing conceptual understanding using inquiry learning without a heavy emphasis on cognitive conflict, making it more appropriate and effective for entry level students and K-12 teachers.

Likewise, Koenig, Wood, Bortner, and Bao ( 2019 ) have developed a lab curriculum that is intentionally designed around the twenty-first Century learning goals for developing cognitive, interpersonal, and intrapersonal abilities. In terms of the cognitive domain, the lab learning outcomes center on critical thinking and scientific reasoning but do so through operationally defined sub-skills, all of which are transferrable across STEM. These selected sub-skills are found in the research literature, and include the ability to control variables and engage in data analytics and causal reasoning. For each targeted sub-skill, a series of pre-lab and in-class activities provide students with repeated, deliberate practice within multiple hypothetical science-based scenarios followed by real inquiry-based lab contexts. This explicit instructional strategy has been shown to be essential for the development of scientific reasoning (Chen & Klahr, 1999 ). In addition, the Karplus Learning Cycle (Karplus, 1964 ) provides the foundation for the structure of the lab activities and involves cycles of exploration, concept introduction, and concept application. The curricular framework is such that as the course progresses, the students engage in increasingly complex tasks, which allow students the opportunity to learn gradually through a progression from simple to complex skills.

As part of this same curriculum, students’ interpersonal skills are developed, in part, through teamwork, as students work in groups of 3 or 4 to address open-ended research questions, such as, What impacts the period of a pendulum? In addition, due to time constraints, students learn early on about the importance of working together in an efficient manor towards a common goal, with one set of written lab records per team submitted after each lab. Checkpoints built into all in-class activities involve Socratic dialogue between the instructor and students and promote oral communication. This use of directed questioning guides students in articulating their reasoning behind decisions and claims made, while supporting the development of scientific reasoning and conceptual understanding in parallel (Hake, 1992 ). Students’ intrapersonal skills, as well as communication skills, are promoted through the submission of individual lab reports. These reports require students to reflect upon their learning over each of four multi-week experiments and synthesize their ideas into evidence-based arguments, which support a claim. Due to the length of several weeks over which students collect data for each of these reports, the ability to organize the data and manage their time becomes essential.

Despite the growing emphasis on research and development of curriculum that targets twenty-first Century learning, converting a traditionally taught lab course into a meaningful inquiry-based learning environment is challenging in current reform efforts. Typically, the biggest challenge is a lack of resources; including faculty time to create or adapt inquiry-based materials for the local setting, training faculty and graduate student instructors who are likely unfamiliar with this approach, and the potential cost of new equipment. Koenig et al. ( 2019 ) addressed these potential implementation barriers by designing curriculum with these challenges in mind. That is, the curriculum was designed as a flexible set of modules that target specific sub-skills, with each module consisting of pre-lab (hypothetical) and in-lab (real) activities. Each module was designed around a curricular framework such that an adopting institution can use the materials as written, or can incorporate their existing equipment and experiments into the framework with minimal effort. Other non-traditional approaches have also been experimented with, such as the work by Sobhanzadeh, Kalman, and Thompson ( 2017 ), which targets typical misconceptions by using conceptual questions to engage students in making a prediction, designing and conducting a related experiment, and determining whether or not the results support the hypothesis.

Another challenge for inquiry labs is the assessment of skills-based learning outcomes. For assessment of scientific reasoning, a new instrument on inquiry in scientific thinking analytics and reasoning (iSTAR) has been developed, which can be easily implemented across large numbers of students as both a pre- and post-test to assess gains. iSTAR assesses reasoning skills necessary in the systematical conduct of scientific inquiry, which includes the ability to explore a problem, formulate and test hypotheses, manipulate and isolate variables, and observe and evaluate the consequences (see www.istarassessment.org ). The new instrument expands upon the commonly used classroom test of scientific reasoning (Lawson, 1978 , 2000 ), which has been identified with a number of validity weaknesses and a ceiling effect for college students (Bao, Xiao, Koenig, & Han, 2018 ).

Many education innovations need supporting infrastructures that can ensure adoption and lasting impact. However, making large-scale changes to current education settings can be risky, if not impossible. New education approaches, therefore, need to be designed to adapt to current environmental constraints. Since higher-end skills are a primary focus of twenty-first Century learning, which are most effectively developed in inquiry-based group settings, transforming current lecture and lab courses into this new format is critical. Although this transformation presents great challenges, promising solutions have already emerged from various research efforts. Perhaps the biggest challenge is for STEM educators and researchers to form an alliance to work together to re-engineer many details of the current education infrastructure in order to overcome the multitude of implementation obstacles.

This paper attempts to identify a few central ideas to provide a broad picture for future research and development in physics education, or STEM education in general, to promote twenty-first Century learning. Through a synthesis of the existing literature within the authors’ limited scope, a number of views surface.

Education is a service to prepare (not to select) the future workforce and should be designed as learner-centered, with the education goals and teaching-learning methods tailored to the needs and characteristics of the learners themselves. Given space constraints, the reader is referred to the meta-analysis conducted by Freeman et al. ( 2014 ), which provides strong support for learner-centered instruction. The changing world of the twenty-first Century informs the establishment of new education goals, which should be used to guide research and development of teaching and learning for present day students. Aligned to twenty-first Century learning, the new science standards have set the goals for STEM education to transition towards promoting deep learning of disciplinary knowledge, thereby building upon decades of research in PER, while fostering a wide range of general high-end cognitive and non-cognitive abilities that are transferable across all disciplines.

Following these education goals, more research is needed to operationally define and assess the desired high-end reasoning abilities. Building on a clear definition with effective assessments, a large number of empirical studies are needed to investigate how high-end abilities can be developed in parallel with deep learning of concepts, such that what is learned can be generalized to impact the development of curriculum and teaching methods which promote skills-based learning across all STEM fields. Specifically for PER, future research should emphasize knowledge integration to promote deep conceptual understanding in physics along with inquiry learning to foster scientific reasoning. Integration of physics learning in contexts that connect to other STEM disciplines is also an area for more research. Cross-cutting, interdisciplinary connections are becoming important features of the future generation physics curriculum and defines how physics should be taught collaboratively with other STEM courses.

This paper proposed meaningful areas for future research that are aligned with clearly defined education goals for twenty-first Century learning. Based on the existing literature, a number of challenges are noted for future directions of research, including the need for:

clear and operational definitions of goals to guide research and practice

concrete operational definitions of high-end abilities for which students are expected to develop

effective assessment methods and instruments to measure high-end abilities and other components of twenty-first Century learning

a knowledge base of the curriculum and teaching and learning environments that effectively support the development of advanced skills

integration of knowledge and ability development regarding within-discipline and cross-discipline learning in STEM

effective means to disseminate successful education practices

The list is by no means exhaustive, but these themes emerge above others. In addition, the high-end abilities discussed in this paper focus primarily on scientific reasoning, which is highly connected to other skills, such as critical thinking, systems thinking, multivariable modeling, computational thinking, design thinking, etc. These abilities are expected to develop in STEM learning, although some may be emphasized more within certain disciplines than others. Due to the limited scope of this paper, not all of these abilities were discussed in detail but should be considered an integral part of STEM learning.

Finally, a metacognitive position on education research is worth reflection. One important understanding is that the fundamental learning mechanism hasn’t changed, although the context in which learning occurs has evolved rapidly as a manifestation of the fast-forwarding technology world. Since learning is a process at the interface between a learner’s mind and the environment, the main focus of educators should always be on the learner’s interaction with the environment, not just the environment. In recent education developments, many new learning platforms have emerged at an exponential rate, such as the massive open online courses (MOOCs), STEM creative labs, and other online learning resources, to name a few. As attractive as these may be, it is risky to indiscriminately follow trends in education technology and commercially-incentivized initiatives before such interventions are shown to be effective by research. Trends come and go but educators foster students who have only a limited time to experience education. Therefore, delivering effective education is a high-stakes task and needs to be carefully and ethically planned and implemented. When game-changing opportunities emerge, one needs to not only consider the winners (and what they can win), but also the impact on all that is involved.

Based on a century of education research, consensus has settled on a fundamental mechanism of teaching and learning, which suggests that knowledge is developed within a learner through constructive processes and that team-based guided scientific inquiry is an effective method for promoting deep learning of content knowledge as well as developing high-end cognitive abilities, such as scientific reasoning. Emerging technology and methods should serve to facilitate (not to replace) such learning by providing more effective education settings and conveniently accessible resources. This is an important relationship that should survive many generations of technological and societal changes in the future to come. From a physicist’s point of view, a fundamental relation like this can be considered the “mechanics” of teaching and learning. Therefore, educators and researchers should hold on to these few fundamental principles without being distracted by the surfacing ripples of the world’s motion forward.

Availability of data and materials

Not applicable.

Abbreviations

American Association of Physics Teachers

Investigative Science Learning Environment

Inquiry in Scientific Thinking Analytics and Reasoning

Massive open online course

New Generation Science Standards

• Physics education research

Science Technology Engineering and Math

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The research is supported in part by NSF Awards DUE-1431908 and DUE-1712238. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

The research is supported in part by NSF Awards DUE-1431908 and DUE-1712238.

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LB developed the concept, wrote a significant portion of the review and position, and synthesized the paper. KK wrote and edited a significant portion of the paper. Both authors read and approved the final manuscript.

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Bao, L., Koenig, K. Physics education research for 21 st century learning. Discip Interdscip Sci Educ Res 1 , 2 (2019). https://doi.org/10.1186/s43031-019-0007-8

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Received : 17 April 2019

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DOI : https://doi.org/10.1186/s43031-019-0007-8

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Unifying model for the rheological behavior of hygroresponsive materials

Júlio o. amando de barros and falk k. wittel, phys. rev. e 109 , 044139 – published 17 april 2024.

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Hygroresponsive materials exhibit a complex structure-to-property relationship. The interactions of water within these materials under varying hygric and mechanical loads play a crucial role in their macroscopic deformation and final application. While multiple models are available in literature, many lack a comprehensive physical understanding of these phenomena. In this paper, we introduce a stick-slip fiber bundle model that captures the fundamental behaviors of hygroresponsive materials. We incorporate moisture-dependent elements and rules governing the initiation and relaxation of slip strains as well as failure to the statistical approach offered by fiber bundle models. The additional features are based on well-founded interpretations of the structure-to-property relationship in cellulosic materials. Slip strains are triggered by changes in load and moisture, as well as by creep deformations. When subjected to moisture cycles, the model accumulates slip strains, resulting in mechanosorptive behavior. When the load is removed, slip strains are partially relaxed, and subsequent moisture cycles trigger further relaxation, as expected from observations with mechanosorptive material. Importantly, these slip strains are not considered plastic strains; instead, they are unified, nonlinear frozen strains, activated by various stimuli. Failure of fibers is defined by a critical number of slip events allowing for an integrated simulation from intact, via damaged, failed states. We investigate the transition between these regimes upon changes in the hygric and mechanical loading history for relevant parameter ranges. Our enhanced stick-slip fiber bundle model increases the understanding of the intricate behavior of hygroresponsive materials and contributes to a more robust framework for analyzing and interpreting their properties.

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Main features observed in hygroresponsive materials. The strain ( ɛ ) is presented in black, while the load ( σ ) and relative humidity (RH) are represented by red, respectively, blue dashed lines. Letters indicate different features like H for hygroexpansion, E for elasticity, VE for viscoelasticity, and MS for mechanosorption.

Micromechanical interpretation of stick-slip dynamics on wood fibers. We present a wood fiber on the left with its microfibril aggregates exposed. Following to the right, the aggregates' interactions and their behavior under mechanical and moisture stimuli are presented. ɛ d and ɛ w are the nonslip strain at the dry state and wet state, respectively, while ɛ d S ,   ɛ w S , and ɛ t S are the slip strains respective to drying, wetting, and time evolution.

Sketch of the FBM with green representing slip, yellow tensile, and red compressive strains. On the right is the rheological representation of a single fiber model with elastic, viscoelastic, hygro-expansive, and slip element in series. The total strain ɛ is present in both representations.

Flowchart of implemented code.

SS-FBM normalized strain over the normalized time. Solid lines represent the normalized strain ɛ / ɛ c , while dashed lines represent the normalized strain without slip ( ɛ − 〈 ɛ S 〉 ) / ɛ c . Line colors resemble softening ratios D w / D d .

Residual normalized stresses (a) and number of slip events (b) for each fiber of the bundle at t = 40 τ in Fig.  5 .

SS-FBM strain over five moisture cycles. Solid lines represent total values of ɛ and ɛ S in both plots, respectively, while dashed lines represent their labeled components.

SS-FBM slip strain evolution over ten moisture cycles and the respective normalized slip (green) and reverse slip (black) avalanches.

Probability density of slip thresholds for increasing moisture cycles MC with identical legend.

SS-FBM maximum strain-stress relation for different dry-to-wet compliance ratios and comparison to the strength with the dry FBM (inset).

Applied load over the number of cycles that can be imposed before the failure of the system. If the system doesn't fail, the maximum number of imposed cycles is 50.

Comparison of the proposed model (SS-FBM) results to empirical data and the hygrolock modeling approach proposed by Dubois et al. [ 42 ].

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