research helps with problem solving

Research does solve real-world problems: experts must work together to make it happen

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Generating knowledge is one of the most exciting aspects of being human. The inventiveness required to apply this knowledge to solve practical problems is perhaps our most distinctive attribute.

But right now we have before us some hairy challenges – whether that be figuring our how to save our coral reefs from warmer water , landing a human on Mars , eliminating the gap in life expectancy between the “haves” and “have-nots” or delivering reliable carbon-free energy .

It’s commonly said that an interdisciplinary approach is vital if we are to tackle such real world challenges. But what does this really mean?

Read more: It takes a community to raise a startup

Listen and read with care and you’ll start to notice that the words crossdisciplinary, multidisciplinary, interdisciplinary and transdisciplinary are used interchangeably. These words describe distinctly different ways of harnessing the power of disciplinary expertise to chart a course into the unknown.

In navigation, the tools and methods matter – choose differently and you’ll end up in a different spot. How we go about creating knowledge and solving problems really matters – it changes not only what questions can be asked and answered but fundamentally shapes what’s possible.

What is a discipline?

For centuries we have organised research within disciplines, and this has delivered extraordinary depths of knowledge.

But what is a discipline? It’s a shared language, an environment in which there’s no need to explain the motivation for one’s work, and in which people have a shared sense of what’s valuable.

For example, my background discipline is optical physics. I know what it’s like to be able to skip down the corridor and say,

“I’ve figured out how we can get broadband flat dispersion - we just need to tailor the radial profile!”

…and have people instantly not just know what I mean, but be able to add their own ideas and drive the work forward.

In long-established disciplines it’s often necessary to focus in a narrow area to be able to extend the limits of knowledge within the time-frame of a PhD. And while it’s rarely obvious at the time what benefits will flow from digging a little deeper, our way of life has been transformed over and over as result.

Disciplines focus talent and so can be amazingly efficient ways of generating knowledge. But they can also be extraordinarily difficult to penetrate from the outside without understanding that discipline’s particular language and shared values.

The current emphasis on real-world impact has sharpened awareness on the need to translate knowledge into outcomes. It has also brought attention to the critical role partnerships with industry and other end-users of research play in this process.

Creating impact across disciplines

Try to solve a problem with the tools of a single discipline alone, and it’s as if you have a hammer - everything starts to look like a nail. It’s usually obvious when expertise from more than one discipline is needed.

Consider a panel of experts drawn from different fields to each apply the tools of their field to a problem that’s been externally framed. This has traditionally been how expertise from the social sciences is brought to bear on challenges in public health or the environment.

This is a crossdisciplinary approach , which can produce powerful outcomes provided that those who posed the question are positioned to make decisions based on the knowledge generated. But the research fields themselves are rarely influenced by this glancing encounter with different approaches to knowledge generation.

Multidisciplinary research involves the application of tools from one discipline to questions from other fields. An example is the application of crystallography, discovered by the Braggs, to unravel the structure of proteins . This requires concepts to transfer across domains, sometimes in real time but usually with a lag of years or decades.

Read more: If we really want an ideas boom, we need more women at the top tiers of science

Interdisciplinary research happens when researchers from different fields come together to pose a challenge that wouldn’t be possible in isolation. One example is the highly transparent optical fibres that underpin intercontinental telecommunication networks.

The knowledge creation that made this possible involved glass chemists, optical physicists and communication engineers coming together to articulate the possible, and develop the shared language required to make it a reality. When fields go on this journey together over decades, new fields are born.

In this example the question itself was clear – how can we harness the transparency of silica glass to create optical transmission systems that can transport large volumes of data over long distances?

But what about the questions we don’t know how to pose because without knowledge of another field we don’t know what’s possible? This line of reasoning leads us into the domain of transdisciplinary research .

Transdisciplinary research requires a willingness to craft new questions – whether because they were considered intractable or because without the inspiration from left field they simply didn’t arise. An example of this is applying photonics to IVF incubators - the idea that it could be possible to “listen” to how embryos experience their environment is unlikely to have arisen without bringing these fields together.

Read more: National Science Statement a positive gesture but lacks policy solutions: experts

In my own field, physics, I discovered that when talking to people from other areas the simple question “what would you like to measure?” quickly led to uncharted territory.

Before long we were usually, together, posing fundamentally new questions and establishing teams to tackle them. This can be scary territory but it’s tremendously rewarding and creates space for creativity and the emergence of disruptive technologies.

Excellence, communication, co-location, funding

One of the best ways of getting out of a disciplinary silo is to take every opportunity to talk to others outside your field. Disciplinary excellence is the starting point to get to the table.

And while disciplinary collaborations can flourish over large distances because they share a language and values, it’s usually true that once you mix disciplines co-location becomes a real asset. Then of course there are the questions of how we fund and organise research concentrations to allow inter- and transdisciplinary research to flourish.

With the increased emphasis on impact, these questions are becoming ever more pressing. Organisations that get this right will thrive.

• Research impact
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Using research to solve real world problems

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By Rebecca Knight

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

In India, a country with a population of 1.2bn, fewer than 30 per cent of citizens have a passport, driver’s licence or other form of identification.

It may seem a minor point, except that the absence of these documents makes it difficult to apply for a bank account, obtain a mobile phone or even receive the government subsidies for education and food that individuals are entitled to.

But according to a new survey led by Arun Sundararajan, an associate professor of information, operations and management sciences at New York University’s Stern School of Business and a group of his students, that is changing. A government-sponsored project that began towards the end of 2010 to give every person in India a unique 12-digit ID number is showing signs of success. If enrolment continues according to projections, Prof Sundararajan reckons that about 300m citizens who previously did not have a portable ID will have one by the end of the year.

“It’s a moon-shot project,” says Prof Sundararajan. “It’s having a transformative impact on the lives of hundreds of millions of people.”

It is also, he hopes, a project that will have a transformative impact on the careers of the dozen MBA students who are working with him on the survey. The survey, which is analysing the impact of India’s Unique Identity project, is part of the Stern Consulting Corps programme.

International trips provide hands-on experience

To try to better prepare students to operate in the global economy, a number of leading business schools have introduced courses for MBAs to embark on international consulting projects.

The courses are designed to give students an applied learning experience that is very different from the one they receive on campus.

Harvard Business School , for instance, recently launched a year-long required course for its first-year students called “Field Immersion Experiences for Leadership Development”, or Field for short. The capstone of the course is a week-long trip to a developing country where student teams work closely with a company to develop an idea for a product or service.

Last year, projects were based in cities including Cape Town, Mumbai, Shanghai, Warsaw and Buenos Aires.

“Our aspiration is that it becomes so self-evident about how valuable this is that other schools do it too,” says Youngme Moon, who chairs HBS’s MBA programme.

Massachusetts Institute of Technology’s Sloan School , meanwhile, has expanded its G-Lab course in which students work with the management of overseas start-ups. Student teams work remotely from MIT for three months and full-time at their host companies for at least three weeks. Last year students worked on projects in Kenya, Colombia, Indonesia and other countries.

While some schools may view courses with an international consulting component as a way to “teach students how to be [a] consultant, it’s very much meant to be an interesting learning challenge,” says Michellana Jester, director of Sloan’s action learning programme.

According to her, business schools are using these courses to strike the right balance between academic rigour and relevance. “Scholarship is important and research is important, but how do you make it relevant for students in business schools today?

“It’s a transition for business schools right now in terms of how we navigate this,” she says.

SCC, an elective course now in its 10th year, began as a programme that placed students with local non-profits on 10-week project engagements. This year, for the first time, students worked on projects in emerging markets linked to faculty research. This new element provides students with vivid illustrations of how academic research can be used to solve real world problems.

“What [students] are gaining from this is an understanding of the potential of business to be an agent of social change,” says Prof Sundararajan. “It’s one thing to be exposed to examples of this in a textbook, it’s another to witness it first hand.”

As top schools strive to infuse their curricula with more hands-on learning experiences by adding overseas exchange programmes and class consulting projects in far-flung corners of the world, SCC stands out for its emphasis on research.

The new focus of the SCC programme reflects the increasing interest from MBA students in using their degrees to work on social policy issues. The programme is popular on campus: more than 100 students participated in the programme this past academic year and applications to the SCC rose 117 per cent this spring compared with last year.

Academic research is often accused of being ponderous, narrow and detached from the real world. But there is a new wave of research coming out of schools today that concerns how government and business can work together to solve big social problems, according to Professor Peter Henry, Stern’s dean.

There is also a growing recognition on the part of management faculty that the type of research they conduct about corporations has potentially broad applications for other kinds of organisations.

“There is a false dichotomy between research and the real world,” says Prof Henry. “Research can have a real impact.”

One group of students, for instance, worked on a business plan for a city that is being developed in Honduras. Because the city is a new concept, the business plan will have a direct effect on policy decisions in the country.

The students, under the supervision of Professor Paul Romer who heads the Urbanization Project, a research centre at Stern that focuses on urban growth and governance in the developing world, spent the semester devising potential scenarios for the city, such as population growth models and potential financial rules and regulations, as well as working up infrastructure estimates and writing policy briefs. Some of their findings were presented in a meeting Prof Romer had with Octavio Sánchez, chief of staff to the Honduran president.

“We’re getting students into the world through the lens of research,” says Prof Henry. “We’re giving students the chance to say, ‘I didn’t just take a set of classes. I built something’.”

Throughout the projects, students work closely with a faculty member and often develop the type of mentoring relationship that has typically been the province of PhD programmes. Teams also work with an outside mentor from a top-tier consulting company.

Students hone their analytical skills, but are also able to practise their professional responsibilities such as meeting a timeline and soliciting feedback from a client.

“The learning experience for the students was better than I expected,” says Prof Romer. “What they learnt was not just how to think abstractly about governance and fiscal policy – the kind of things you have to think about when you’re creating a new city – but also how will you work in teams, how will you divide tasks, what will you do when one sub-team gets stuck.”

The fact that students were working on a project with real world implications “lent more urgency to the group effort”, he says. “People are taking real decisions on [the students’ work]. A term paper doesn’t really matter. This goes out of the realm of a pretend exercise and makes it real.”

For students, the risk of working on such an engrossing project is that it can make other business school assignments seem dull or unworthy by comparison.

But Benjamin Wise, a student who worked on the Honduras project, says that it forced him to think about how to apply concepts learnt in the classroom.

“I’d be sitting in my corporate finance class on the edge of my seat because we were learning about a financial model that I couldn’t wait to plug in to [one of the models in] my project,” he says.

Prof Sundararajan says that spending a week in India was an eye-opening experience for his students.

“I could see them immersed in the context. They gained a clearer understanding of the breadth of vision and they could see what the problem was.

“This is the kind of immersive experiential learning that alters the worldview of students.”

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Research to Solve Problems

The 2023 SSRC Centennial Lectures showcased a shared focus on not only describing problems, but also finding solutions to those problems. SSRC President Anna Harvey reflects on what it means to pursue research designed to solve problems, and how the SSRC can most effectively support problem-solving research.

During 2023, the Centennial year for the Social Science Research Council, we hosted ten Centennial lectures showcasing recent social and behavioral science on ten social problems central to the Council’s 100-year history of work: immigration , social insurance , racial discrimination , economic development , the green revolution , education , persistent poverty , climate change , democracy , and pandemic response (the recorded lectures are all available here ). A striking feature of these lectures was a shared focus on not only describing problems, but also finding solutions to those problems.

What does it mean to solve problems using social and behavioral science? In the first instance, the researchers who gave our Centennial lectures had all identified patterns of human decision making on questions such as migration, health, or employment that presented problems, in the sense that there plausibly existed decisions that could produce better outcomes for more people: e.g., higher incomes, less disease, less conflict. Further, these problems of decision making were all potentially responsive to the resources generally available to large organizations: e.g., money, information, attention, status. Alternative allocations of these resources could potentially shift decision making in ways that produced more societally beneficial outcomes. The researchers who gave our Centennial lectures were all looking for interventions that, by leveraging alternative allocations of available resources, could induce more societally beneficial decisions and actions.

But our Centennial lecturers weren’t seeking just any interventions that would work in this way. They were looking for effective interventions with a decent chance of being implemented into policy and practice at scale. From the very beginning of their research projects, they had looked down the path from identifying a problem of decision making, to developing a potential solution, to evaluating that solution, to implementation at scale by large-footprint organizations–governments, firms, large NGOs–and had designed their projects so as to maximize their chances of success at the end of that path. 

The interventions that were likely to be both effective and adopted at scale shared similar features, features that have also been identified in the implementation science literature: they were likely to be low cost, and not increasing in cost as they were scaled to larger populations ( Kremer et al 2021 , List 2022 ). They were likely to have been developed by or in partnership with a client organization, and designed to be integrated into the organization’s existing workflow ( Kremer et al 2021 , DellaVigna et al 2022 , Bonargent 2023 ). Their effectiveness was likely to have been evaluated using rigorous methods, including in some cases multiple small-scale evaluations, to guard against single-trial false positives at the pilot stage ( Kremer et al 2021 , List 2022 ).

Designing problem-solving research with implementation in mind at inception has clear benefits in a world of scarce research funds for social and behavioral science . Spending valuable research funds on evaluating potential solutions that have little chance of being implemented at scale, even if effective in small samples (e.g., because of increasing marginal costs), is not an efficient use of the research community’s limited resources. Building feasible implementation into research designs at inception is a potentially more cost-effective way to spend our scarce funding resources.

As we enter the Social Science Research Council’s second century of work, we have been reflecting on how the Council can best support social and behavioral science aimed at solving important problems. We have been reading the research on the research-to-policy pipeline, and have found much there that can guide our work. Our new website features the research undergirding our programs to support problem-solving social and behavioral science:

• we support fellowships giving researchers the time and freedom to pursue novel ideas, because unrestricted research funds are more likely to result in innovative policy solutions;
• we administer research grants underwriting the evaluation of new policy solutions, because rigorous evaluation increases the likelihood that effective solutions will be implemented at scale; 
• we host in-person convenings to share research-backed policy solutions with stakeholders and to incubate new research agendas, because such events lead to increased evidence uptake and to more productive collaborations; 
• we produce curated and accessible online knowledge platforms and technical reports , because these communication strategies increase the likelihood that research-based policy solutions will be widely adopted; 
• we offer mentoring programs to researchers underrepresented in their disciplines, because mentoring is a proven strategy to broaden problem-solving research opportunities.

As we launch the Council’s second century, we also look forward to engaging with new research on problem solving. This year’s College and University Fund Lecture Series will showcase new research illuminating practices increasing the likelihood that research-backed solutions will be implemented at scale; we look forward to sharing more information about this lecture series.

And finally, in 2024 we will be exploring new strategies to deploy the SSRC’s Agenda Fund to provide opportunities for researchers to use social and behavioral science to innovate new solutions to our most pressing social problems. Stay tuned for announcements of these upcoming opportunities.

Best wishes for a happy, healthy, and productive New Year!

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10 Reasons Why Research is Important

No matter what career field you’re in or how high up you are, there’s always more to learn . The same applies to your personal life. No matter how many experiences you have or how diverse your social circle, there are things you don’t know. Research unlocks the unknowns, lets you explore the world from different perspectives, and fuels a deeper understanding. In some areas, research is an essential part of success. In others, it may not be absolutely necessary, but it has many benefits. Here are ten reasons why research is important:

#1. Research expands your knowledge base

The most obvious reason to do research is that you’ll learn more. There’s always more to learn about a topic, even if you are already well-versed in it. If you aren’t, research allows you to build on any personal experience you have with the subject. The process of research opens up new opportunities for learning and growth.

#2. Research gives you the latest information

Research encourages you to find the most recent information available . In certain fields, especially scientific ones, there’s always new information and discoveries being made. Staying updated prevents you from falling behind and giving info that’s inaccurate or doesn’t paint the whole picture. With the latest info, you’ll be better equipped to talk about a subject and build on ideas.

#3. Research helps you know what you’re up against

In business, you’ll have competition. Researching your competitors and what they’re up to helps you formulate your plans and strategies. You can figure out what sets you apart. In other types of research, like medicine, your research might identify diseases, classify symptoms, and come up with ways to tackle them. Even if your “enemy” isn’t an actual person or competitor, there’s always some kind of antagonist force or problem that research can help you deal with.

#4. Research builds your credibility

People will take what you have to say more seriously when they can tell you’re informed. Doing research gives you a solid foundation on which you can build your ideas and opinions. You can speak with confidence about what you know is accurate. When you’ve done the research, it’s much harder for someone to poke holes in what you’re saying. Your research should be focused on the best sources. If your “research” consists of opinions from non-experts, you won’t be very credible. When your research is good, though, people are more likely to pay attention.

#5. Research helps you narrow your scope

When you’re circling a topic for the first time, you might not be exactly sure where to start. Most of the time, the amount of work ahead of you is overwhelming. Whether you’re writing a paper or formulating a business plan, it’s important to narrow the scope at some point. Research helps you identify the most unique and/or important themes. You can choose the themes that fit best with the project and its goals.

#6. Research teaches you better discernment

Doing a lot of research helps you sift through low-quality and high-quality information. The more research you do on a topic, the better you’ll get at discerning what’s accurate and what’s not. You’ll also get better at discerning the gray areas where information may be technically correct but used to draw questionable conclusions.

#7. Research introduces you to new ideas

You may already have opinions and ideas about a topic when you start researching. The more you research, the more viewpoints you’ll come across. This encourages you to entertain new ideas and perhaps take a closer look at yours. You might change your mind about something or, at least, figure out how to position your ideas as the best ones.

#8. Research helps with problem-solving

Whether it’s a personal or professional problem, it helps to look outside yourself for help. Depending on what the issue is, your research can focus on what others have done before. You might just need more information, so you can make an informed plan of attack and an informed decision. When you know you’ve collected good information, you’ll feel much more confident in your solution.

#9. Research helps you reach people

Research is used to help raise awareness of issues like climate change , racial discrimination, gender inequality , and more. Without hard facts, it’s very difficult to prove that climate change is getting worse or that gender inequality isn’t progressing as quickly as it should. The public needs to know what the facts are, so they have a clear idea of what “getting worse” or “not progressing” actually means. Research also entails going beyond the raw data and sharing real-life stories that have a more personal impact on people.

#10. Research encourages curiosity

Having curiosity and a love of learning take you far in life. Research opens you up to different opinions and new ideas. It also builds discerning and analytical skills. The research process rewards curiosity. When you’re committed to learning, you’re always in a place of growth. Curiosity is also good for your health. Studies show curiosity is associated with higher levels of positivity, better satisfaction with life, and lower anxiety.

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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3 Simple Strategies to Improve Students’ Problem-Solving Skills

These strategies are designed to make sure students have a good understanding of problems before attempting to solve them.

Research provides a striking revelation about problem solvers. The best problem solvers approach problems much differently than novices. For instance, one meta-study showed that when experts evaluate graphs , they tend to spend less time on tasks and answer choices and more time on evaluating the axes’ labels and the relationships of variables within the graphs. In other words, they spend more time up front making sense of the data before moving to addressing the task.

While slower in solving problems, experts use this additional up-front time to more efficiently and effectively solve the problem. In one study, researchers found that experts were much better at “information extraction” or pulling the information they needed to solve the problem later in the problem than novices. This was due to the fact that they started a problem-solving process by evaluating specific assumptions within problems, asking predictive questions, and then comparing and contrasting their predictions with results. For example, expert problem solvers look at the problem context and ask a number of questions:

• What do we know about the context of the problem?
• What assumptions are underlying the problem? What’s the story here?
• What qualitative and quantitative information is pertinent?
• What might the problem context be telling us? What questions arise from the information we are reading or reviewing?
• What are important trends and patterns?

As such, expert problem solvers don’t jump to the presented problem or rush to solutions. They invest the time necessary to make sense of the problem.

Now, think about your own students: Do they immediately jump to the question, or do they take time to understand the problem context? Do they identify the relevant variables, look for patterns, and then focus on the specific tasks?

If your students are struggling to develop the habit of sense-making in a problem- solving context, this is a perfect time to incorporate a few short and sharp strategies to support them.

3 Ways to Improve Student Problem-Solving

1. Slow reveal graphs: The brilliant strategy crafted by K–8 math specialist Jenna Laib and her colleagues provides teachers with an opportunity to gradually display complex graphical information and build students’ questioning, sense-making, and evaluating predictions.

For instance, in one third-grade class, students are given a bar graph without any labels or identifying information except for bars emerging from a horizontal line on the bottom of the slide. Over time, students learn about the categories on the x -axis (types of animals) and the quantities specified on the y -axis (number of baby teeth).

The graphs and the topics range in complexity from studying the standard deviation of temperatures in Antarctica to the use of scatterplots to compare working hours across OECD (Organization for Economic Cooperation and Development) countries. The website offers a number of graphs on Google Slides and suggests questions that teachers may ask students. Furthermore, this site allows teachers to search by type of graph (e.g., scatterplot) or topic (e.g., social justice).

2. Three reads: The three-reads strategy tasks students with evaluating a word problem in three different ways . First, students encounter a problem without having access to the question—for instance, “There are 20 kangaroos on the grassland. Three hop away.” Students are expected to discuss the context of the problem without emphasizing the quantities. For instance, a student may say, “We know that there are a total amount of kangaroos, and the total shrinks because some kangaroos hop away.”

Next, students discuss the important quantities and what questions may be generated. Finally, students receive and address the actual problem. Here they can both evaluate how close their predicted questions were from the actual questions and solve the actual problem.

To get started, consider using the numberless word problems on educator Brian Bushart’s site . For those teaching high school, consider using your own textbook word problems for this activity. Simply create three slides to present to students that include context (e.g., on the first slide state, “A salesman sold twice as much pears in the afternoon as in the morning”). The second slide would include quantities (e.g., “He sold 360 kilograms of pears”), and the third slide would include the actual question (e.g., “How many kilograms did he sell in the morning and how many in the afternoon?”). One additional suggestion for teams to consider is to have students solve the questions they generated before revealing the actual question.

3. Three-Act Tasks: Originally created by Dan Meyer, three-act tasks follow the three acts of a story . The first act is typically called the “setup,” followed by the “confrontation” and then the “resolution.”

This storyline process can be used in mathematics in which students encounter a contextual problem (e.g., a pool is being filled with soda). Here students work to identify the important aspects of the problem. During the second act, students build knowledge and skill to solve the problem (e.g., they learn how to calculate the volume of particular spaces). Finally, students solve the problem and evaluate their answers (e.g., how close were their calculations to the actual specifications of the pool and the amount of liquid that filled it).

Often, teachers add a fourth act (i.e., “the sequel”), in which students encounter a similar problem but in a different context (e.g., they have to estimate the volume of a lava lamp). There are also a number of elementary examples that have been developed by math teachers including GFletchy , which offers pre-kindergarten to middle school activities including counting squares , peas in a pod , and shark bait .

Students need to learn how to slow down and think through a problem context. The aforementioned strategies are quick ways teachers can begin to support students in developing the habits needed to effectively and efficiently tackle complex problem-solving.

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Can Video Gameplay Improve Undergraduates’ Problem-Solving Skills?

Benjamin emihovich.

University of Michigan - Flint, Flint, USA

Nelson Roque

Pennsylvania State University, State College, USA

Justin Mason

University of Florida, Gainesville, USA

In this study, the authors investigated if two distinct types of video gameplay improved undergraduates’ problem-solving skills. Two groups of student participants were recruited to play either a roleplaying video game (World of Warcraft; experimental group) or a brain-training video game (CogniFit; control group). Participants were measured on their problem-solving skills before and after 20 hours of video gameplay. Two measures were used to assess problem-solving skills for this study, the Tower of Hanoi and The PISA Problem Solving Test. The Tower of Hanoi measured the rule application component of problem-solving skills and the PISA Problem Solving test measured transfer of problem-solving skills from video gameplay to novel scenarios on the test. No significant differences were found between the two groups on either problem-solving measure. Implications for future studies on game- based learning are discussed.

Introduction

Video games are played by more than half of the U.S population and the video game industry generated $36 billion in 2018 ( ESA, 2018 ). Given the popularity and success of the video game industry, game- based scholars are exploring how well-designed video games can be used to improve a wide range of knowledge, skills, and abilities referred to as game-based learning (GBL). Proponents of GBL argue that well-designed video games are grounded by active participation and interaction as the focal point of the learner experience and can lead to changes in behavior and cognition ( Ifenthaler, Eseryel, & Ge, 2012 ; Shute et al., 2019 ). Moreover, well-designed video games immerse players in environments that can provide a framework for learning experiences by promoting engagement and transfer from simulated worlds to the natural world ( Dede, 2009 ).

Current American students are not receiving adequate exposure to authentic ill-structured problem-solving scenarios in their classrooms, and schools need to address the acquisition of problem-solving skills for students in the 21st century ( Shute & Wang, 2016 ). American students trail their international counterparts in problem-solving skills on the Program for International Student Assessment (PISA) Problem Solving Test. Furthermore, American business leaders complain about recent college graduates’ lack of problem-solving skills. Two surveys conducted by the Association of American Colleges and Universities of business leaders and students indicated that problem-solving skills are increasingly desirable for American employers, but only 38% of employers reported that recently hired American college graduates could analyze and solve complex problems while working ( Hart Associates, 2018 ).

Researchers of video game studies find that gameplay can be positively associated with the improvement of problem-solving skills ( Shute, Ventura, & Ke, 2015 ; Spires et al., 2011 ). However, current discourse in the field of gameplay and problem-solving skills centers primarily on descriptive research ( Eseryel et al., 2014 ) which can be summarized based on the following premise: video games require players to solve problems, and over time, playing video games will lead to improved problem- solving skills ( Hung & Van Eck, 2010 ). Descriptive research is important to argue that video games support problem-solving skills, but further empirical research is needed to demonstrate whether problem-solving skills are acquired through video gameplay. This research study addressed whether two distinct types of video gameplay empirically affects undergraduates’ problem-solving skills.

Video Games and Problem-Solving Skills

According to Mayer and Wittrock’s (2006) definition, problem solving includes four central characteristics: (1) occurs internally to the problem solver’s cognitive system; (2) is a process that involves conceptualizing and manipulating knowledge; (3) is goal directed; and (4) is dependent on the knowledge and skills of the problem solver to establish the difficulty in which obstacles must be overcome to reach a solution. Unlike the well-structured problems that students face in formal learning settings, well-designed games provide students with challenging scenarios that promote problem-solving skills by requiring players to generate new knowledge from challenging scenarios within interactive environments, while also providing immersive gameplay that includes ongoing feedback for the players to hone their problem-solving skills over time ( Van Eck, Shute, & Rieber, 2017 ). Rules govern video gameplay mechanics and one component of problem solving is the ability to apply existing rules in the problem space known as rule application ( Shute et al., 2015 ). One example of a rule application is found in the well-researched problem-solving puzzle the Tower of Hanoi ( Huyck & Kreivenas, 2018 ; Schiff & Vakil, 2015 ; TOH, 2019 ). The rule application component of problem-solving skill is one of the dependent variables in this study. Rule application refers to the problem-solver’s representation of the problem space through direct action, which is critical to problem solving ( Van Eck et al., 2017 ).

Literature Review

Video gameplay and transfer.

Researchers contend that the hidden power of well-designed video games is their potential to address higher-level learning, like retention, transfer, and problem-solving skills ( Gee, 2008 ; Shute & Wang, 2015 ). Retention is the ability to remember the presented information and correctly recall it when needed, while transfer is the ability to apply previously learned information in a novel situation ( Stiller & Schworm, 2019 ). Possible outcomes of playing video games may include the improvement of collaborative problem-solving skills, confidence, and leadership skills that are transferable to the workforce environment. Recent research on video game training studies and transfer of cognitive and noncognitive skills indicates that gameplay is positively associated with the improvement of attention, problem-solving skills, persistence ( Green & Bavelier, 2012 ; Rowe et al., 2011 ; Shute et al., 2015 ; Ventura et al., 2013 ), executive functions ( Oei & Patterson, 2014 ), and hypothesis testing strategies ( Spires et al., 2011 ). However, other researchers have found null effects of video gameplay and transfer of cognitive skills ( Ackerman, et al., 2010 ; Baniqued, Kranz, et al., 2013 ; Boot et al., 2008 ).

A recent meta-analysis of brain-training interventions found that brain-training interventions can improve performance on trained tasks but there were fewer examples of interventions indicating improved performance on closely related tasks, and minimal evidence that training enhances performance on daily cognitive abilities ( Simons et al., 2016 ). Among those finding null effects, questions were raised about the methodological shortcomings of video game training and transfer studies that are common pitfalls in experimental trials. Some of the pitfalls included failing to report full methods used in a study and lack of an effective active control condition that can expect to see similar improvement in competencies as the experimental group ( Baniqued et al., 2013 ; Boot, 2015 ; Boot, Blakely & Simons, 2011 ). Unless researchers define recruitment methods for participants and their gaming expertise (novice vs. expert), as well as compare active control groups with experimental groups receiving equal training games, then differential improvement is indeterminable ( Boot et al., 2013 ; Shute et al., 2015 ). The recruitment approach is outlined in the Method section.

Motivation for Selection of Games

The video games selected for this research study were based on the problem-solving skills players exercise and acquire through gameplay that were aligned with the problem-solving skills assessed on the external measures, the PISA Problem Solving Test and the Tower of Hanoi (TOH). Well-designed video games include sound learning principles embedded within gameplay such as requiring players to solve complex problems which can then be applied to other learning contexts ( Lieberman et al., 2014 ). In this study, the authors examined the effects of playing World of Warcraft ( Activision Blizzard, 2019 ) and CogniFit ( CogniFit, 2019 ) for twenty hours on undergraduates’ problem-solving skills (rule application and problem-solving transfer). The inclusion of CogniFit addresses a main concern of game-based research which is the lack of an active control condition to determine differential improvement ( Boot et al., 2013 ).

Problem-Solving and Video Gameplay Model

The authors have identified observable in-game behaviors (i.e., indicators) during gameplay that provide evidence for each of the problem-solving processes on the PISA Problem Solving Test. The process included playing each video game extensively, checking community forums for solutions to the most challenging problems for each game, and viewing experts’ gameplay video channel streams on YouTube. After generating a list of credible indicators, those selected were based on the following criteria: (a) relevance to the PISA problem solving levels of proficiency and (b) verifiable through gameplay mechanics. Examples of indicators for the PISA problem-solving processes for each game are listed in Tables 1 and ​ and2. 2 . The purpose of developing the problem-solving behavior model is to operationalize the indicators of gameplay that align with the cognitive processes being assessed on the PISA test (i.e., Exploring and Understanding, Representing and Formulating). The PISA Problem Solving Test contains questions representing six levels of proficiency: Level 1 is the most limited form of problem-solving ability such as rule application (solving problems with simple rules or constraints) and Level 6 is the complex form of problem-solving ability (executing strategies and developing mental models to solve problems). The PISA test will determine whether there is transfer of problem-solving skills from video gameplay to novel scenarios.

Examples of indicators for each PISA problem-solving process in Warcraft

Examples of indicators for each PISA problem-solving process in CogniFit

World of warcraft

Massive multiplayer online role-playing games (MMORPGs) require players to manage resources, adapt playstyle to the environment, test new skills and abilities, identify and apply rules to solve problems as well as explore the story of the game through questing. MMORPGs like Warcraft provide gameplay experiences that are analogous to meaningful instruction by offering complex multifaceted problems that require model-based reasoning—understanding interrelated components of a system, and feedback mechanisms among the components to find the best solutions to problems that arise using available tools and resources in a given environment ( Chinn & Malhotra, 2002 ; Steinkuehler & Chmiel, 2006 ). Therefore, if MMORPGs provide an authentic sense of inquiry into solving problems through gameplay, then it is worth testing whether these gameplay experiences transfer to novel problem-solving scenarios.

One specific example of transfer from gameplay in the MMORPG Warcraft to a natural context concerns the problem of reducing travel time. When players enter the game environment, they must account for extended travel time between different activities such as exploration, questing, and combat. To solve this problem, players are given a tool that can be accessed on their user interface by pressing (M) on their keyboard, which opens the map. Listed on the map are designated flight paths (FPs) that act as a taxi service for players. The image in Figure 1 indicates the various FPs a player has unlocked on their world map as well as those that have yet to be discovered ( Activision Blizzard, 2019 ). The flight path is a handy tool because it connects the goal of completing quests as soon as possible to earn rewards with the knowledge that using flight paths greatly reduces travel time between quests. Greatly reducing travel time results in a more efficient way to complete many of the sub goals in the game, and as noted by Shute and Wang (2016) the use of tools and resources efficiently is an important part of problem solving during gameplay.

Player map listing flight path locations in World of Warcraft (2019)

Now, consider one of the questions being assessed on an external measure in the study, the PISA Problem Solving Test. Individuals are given a map that shows the roads between each city, a partially filled-in key that shows distances between cities in kilometers, and the overall layout of the area. The purpose of this question is to assess how individuals calculate the shortest distance from one city to another. To solve the problem, individuals are required to calculate the distance between the two cities of Nuben and Kado using the resources available. This is the same kind of problem that Warcraft players experience during gameplay when travelling between locations to complete quests. Both problem scenarios share the same overlapping components, the ability of the problem solver to use given tools and resources efficiently to find the most direct route that reduces travel time between two separate locations. Figure 2 illustrates this problem scenario on the PISA test ( OECD, 2003 ).

Problem scenario for planning the best route for a trip from PISA (2003)

The brain training game CogniFit claims to have developed a patented system that measures, trains, and monitors cognitive skills like rule application, attention, memory, and visual perception and their relation to neurological pathologies. According to the CogniFit (2019) website the company states there are transfer effects from their mini games to problem solving in the natural world. The brain training game is selected as an active control condition based on this claim as well as repeated practice of rule application embedded into the gameplay experience.

One example of rule application in the brain training game CogniFit occurs in the mini-game Gem Breaker 3D. This mini-game requires players to direct a paddle back and forth across the screen to bounce a ball off the paddle that breaks the gem blocks without letting the ball touch the bottom of the screen. The initial tutorial informs players that improvement of their hand-eye coordination and processing speed skills are emphasized through gameplay with over 100 levels available to master. Feedback is provided to players with a score for each level showing where they can improve. Once all gem blocks are broken the level is completed and a new level begins. However, each player only has access to 4 balls for each level, and if they lose, the game reverts to the beginning. The tutorial shows players how to use the mouse to control the paddle back and forth across the screen while the spacebar launches the ball. Once a gem is broken there is a chance for a power-up to be gained such as shooting multiple balls, explosives, missiles, side quests or power-ups. Figure 3 illustrates the rules of the mini-game in Gem Breaker 3D ( CogniFit, 2019 ).

Rules for the mini-game Gem Breaker 3D listed in the initial tutorial (2019)

Rule application occurs when playing the TOH and requires one to move an entire stack of disks (i.e., a number between 3 and 8) of varied sizes from one of three rods to another. While playing, players are constrained by the following rules: (1) only one disk can be moved at a time; (2) no disk can be placed on a smaller one; (3) only the uppermost disk can be moved on a stack. Rule application is demonstrated by the problem solver in the TOH by configuring the disks and the rods to reach a solution in the problem space. By configuring the disks onto the rods, each move of a disk indicates the problem solver attempting to creatively apply the rules, which is vital to problem solving ( Shute et al., 2019 ). Figure 4 illustrates the problem space in an online version of the TOH (2019) .

Problem space in an online version of the Tower of Hanoi puzzle with 5 disks (2019)

Both video games require players to apply rules to solve problems and rule application is a component of problem solving ( Van Eck et al., 2017 ). As an example, Warcraft players learn that they can only cast certain spells in combat while standing still or that eating and drinking food while sitting down hastens the regeneration of health. Similarly, when playing the mini-game Gem Breaker 3D in CogniFit players use a paddle and a ball to break bricks. One of the first rules players encounter in the game is that they can only move the paddle left or right across the screen or that bonus bricks have special effects like increasing ball speed. The rules are more explicit in CogniFit than Warcraft so brain-training gameplay may promote better performance on solving the TOH. Each move with the paddle and ball is an example of applying the rules, and this is frequently done during gameplay in CogniFit .

However, CogniFit mini-games lack some of the salient gameplay features in Warcraft such as roleplaying gameplay, meaningful interactions with other players, and richly designed problem spaces that GBL scholars suggest are important to the transfer of problem-solving skills from video gameplay to novel contexts measured on the PISA Problem Solving Test. Warcraft gameplay provides players with repeated practice to solve authentic ill-structured problems in rich detailed problem-solving scenarios that may be better suited for transfer to novel scenarios on the test.

Research Questions

After describing the video gameplay conditions of Warcraft and CogniFit as well as reviewing the literature on problem-solving skills, the authors seek to answer the following research questions:

• Is there a change, from pretest to posttest, on the rule-application component of problem solving, after 20 hours of video gameplay, on either a role playing or brain-training video game?
• Does an immersive, collaborative role-playing video game promote transfer of problem-solving skills to novel scenarios better than a brain-training video game for undergraduates after 20 hours of video gameplay?

Setting and Participants

For this study, 91 undergraduate student participants (M Age = 19.32; SD Age = 1.43) were recruited to participate in this study and completed the initial questionnaire for the study, assessing: age, gender, ethnicity, major, and video games played daily. Participants were not invited to participate if they were not students at the data-collecting institution, were not 18–23 years old, or if they reported playing 30 or more minutes of Warcraft or CogniFit . 56 participants were randomly assigned to either the experimental group Warcraft or the control group CogniFit , yet only 34 completed the study ( n = 17 per group). Participant attrition for both groups were attributed to lack of time to complete the study or being too busy with schoolwork. Given the nature of our research questions assessing change as a function of training, subsequently presented analyses only include data from the 34 participants (17 males and 17 females) who completed the study (M Age = 19.44; SD Age = 1.41).

The independent variable in this research study is the video game with two levels: a roleplaying video game ( Warcraft ) and a brain-training video game ( CogniFit ). The video games provide players with repeated problem-solving scenarios requiring players to engage in problem-solving processes. The dependent variable measured for this study is problem-solving skill. One measure assessed the component of rule application of problem solving to solve a puzzle which is the TOH. The second measure assessed problem-solving in novel scenarios which is the PISA Problem Solving Test. Both groups were assessed on the TOH and the PISA Problem Solving Test. The TOH was used to assess research question 1 and the PISA Problem Solving Test was used to assess research question 2.

The Tower of Hanoi

Recall, the TOH is a valid and reliable experimental paradigm that can be used to assess rule application, problem solving and transfer ( Huyck & Kreivenas, 2018 ; Schiff & Vakil, 2015 ). Rule application is demonstrated by the problem solver in the TOH by configuring the disks and the rods to reach a solution in the problem space. By configuring the disks on to the rods, each move of a disk indicates the problem solver attempting to creatively apply the rules. Participants played the TOH on a computer from a free website online. The test score (i.e., lower scores are better) for completing the TOH can range anywhere from 31 (which is the minimal number of moves to execute) until it is solved.

PISA Problem Solving Test

The second external problem-solving measure in this study is the (2003) version of the PISA Problem Solving Test. The PISA Problem Solving Test ( OECD, 2003 ) contains 10 novel problem-solving scenarios, and within each scenario there is a range of one to three different questions that must be solved. There are 19 total questions on the test across all scenarios that required students to solve problems. For this study, participants completed five novel problem-solving scenarios for the pretest and the remaining five novel problem-solving scenarios for the posttest. The levels of proficiency for each question are randomized across all problem-solving scenarios. Each problem-solving scenario is independent from one another and each of the 19 questions across all scenarios being assessed in this study are isomorphic from the questions that were implemented in 2003. The scoring for most questions was either correct or incorrect, with some questions allowing for partially correct answers. Participants that answered each question correctly were awarded one point, while partially correct answers awarded participants a half-point.

Participants for this study were recruited via flyers posted publicly on campus and dormitory bulletin boards. Over the course of eight weeks, participants engaged in 10 gameplay sessions that lasted two hours each. Participants had the opportunity to complete these 10 sessions in two-hour time-blocks that were made available Monday through Friday for eight consecutive weeks. Participants completed the experiment in a classroom lab on campus at the university. In this experiment, student participants were randomly assigned to play one of two video games.

Participants in the experimental condition played the popular roleplaying video game Warcraft that promotes learning new terminologies, mastering interrelated skills and abilities, applying rules to solve problems, goal setting, and reflecting on progress. In addition, participants in the active control condition played the brain-training video game CogniFit (2019) . The video game allows players to select various mini-games including Gem Breaker 3D that may enhance cognitive abilities including rule application, memory, and focus. Student participants in this study were guided by discovery learning and provided with in-game tutorials for each condition while learning to solve problems through active exploration, interacting with the game environment and self-direction ( Westera, 2019 ). At pre-test and post-test participants had 20 minutes to complete isomorphic versions of the TOH as many times as possible. All participants successfully completed the TOH once during the pretest and once during the posttest. At pre-test and post-test, participants also had 20 minutes to complete as many questions as possible on The PISA Problem Solving Test. The pretest required participants to answer nine questions and the posttest required participants to answer 10 questions from multiple problem-based scenarios. Each problem-based scenario was unique, and some examples included the following: (1) calculating the distance between two points given a map; (2) developing a decision tree diagram of a library loan system; and (3) calculating daily energy needs for an individual given a set menu.

Data Structure and Analyses

The full dataset used for all analyses to be presented, contained data from 34 participants. All participants attempted three parallel, computerized forms of the TOH at baseline and at the end of the intervention. Due to the nature of the task’s programming, if participants did not complete a TOH task, the total number of moves attempted was not output to the data file. This will be expanded upon in the results section by utilizing three analyses which included an independent t-test comparing the mean number of incomplete TOH games between the groups, an independent t-test comparing the mean gain score of TOH between the groups, and a multiple linear regression predicting max gain score of TOH by group, by gain score count, and by group, gain score count, and PISA gain. All analyses in sections below were completed in R, version 3.4.3. Packages used for data analysis include: dplyr , for data wrangling ( Wickham et al., 2019 ), and ggplot2 for visualizations ( Wickham, 2016 ), and MASS for stepwise regression analyses ( Venables & Ripley, 2002 ).

Assessing Group differences in Completion

Although groups differed on the overall number of incomplete TOH sessions at pre-testing (N COGNITIVE = 13; N GAMING = 8), an independent t-test of the average number of incomplete games by group, was not significant (p > .05). Furthermore, an independent t-test revealed no group differences for the overall number of incomplete TOH sessions at post-testing (N COGNITIVE = 3; N GAMING = 2; p > .05). A repeated-measures ANOVA revealed a significant time effect, F(1,32) = 13.386, p<.001. However, group, F(1,32) = 1.609, p=.214, nor group by time interaction were significant, F(1,32)=.837, p=.367. On average, participants completed an additional half TOH session (i.e., .47, SD = .53) after receiving either training package (M Pre = .62, SD = .70; M Post = .15, SD = .36). Table 3 shows the means and standard deviations for the pretest and posttest scores participants completed in the experimental ( Warcraft ) and control ( CogniFit ) groups. The mean scores in the table indicate how many moves on average each participant could successfully solve the puzzle per group. For this study, participants had 20 minutes to complete as many questions as possible for the pretest and 20 minutes to do the same for an isomorphic version of the posttest. Table 4 shows the means and standard deviations for the PISA pretest and posttest scores of participants in the experimental ( Warcraft ) and control ( CogniFit ) groups.

Pretest and posttest scores by group on the Tower of Hanoi

Pretest and posttest scores by group on the PISA Problem Solving Test

Quantifying Improvement in Performance

In order to quantify improvement after the intervention, gain scores were calculated by the following formula, for each instance of the TOH task encountered (i.e. three sessions):

Gain scores produced from this calculation can be interpreted as follows: negative gain scores indicating improvement (fewer total moves at post-testing), and positive gain scores indicating a decrement in performance (more total moves at post-testing). As a result of incomplete games not producing the number of moves, for some participants, no gain score calculation was possible. At pretesting, the cognitive training group had three missing gain scores for the second TOH and 10 for the third TOH whereas the game training group had one missing gain score for the second TOH and seven for the third TOH. To account for this, when calculating average gain scores for each participant, averages were weighted by the number of completed games (i.e. averaging by the number of incomplete sessions would result in an undefined calculation, as some participants completed all sessions). Table 5 shows the results of an unpaired t-test on the average weighted gain scores found no group differences in TOH gain scores ( p > .05). Additionally, an unpaired t-test on the average PISA gain scores found no group differences gain scores ( p > .05).

Problem solving performance compared across training groups

Sensitivity Analysis

Due to missing data issues discussed above, the final analysis involves a stepwise multiple linear regression (forward and backward; AIC used for final model variable selection conducted using R package MASS, function stepAIC; Venables & Ripley, 2002 ), predicting max gain score (max of all three potential gain scores) by group membership (WoW or Cognitive Training), total gain score count, and a gain score derived from pre and post measurements on the PISA task (2003). Based on the stepwise regression procedure analysis results in Table 6 , the best fitting, significant, multiple regression model was found to be a model predicting max gain score from gain score count (no predictor for group membership or PISA gain score; F(1,32) = 14.41; p < .001; R 2 = .3104; adjusted R 2 = 0.2889). Participants predicted max gain score is equal to −111.70 + 48.87 (Gain Count), where gain score is in the unit of number of moves. Max gain score increased by 48.87 for every one unit increase in gain score count (more gain scores, closer to 0; less improvement after the intervention). Gain score count was a significant predictor of max gain score (t=3.796; p < 0.001), indicating potential practice effects from repeated exposure to the task. Practice effects will be discussed in subsequent sections.

Stepwise regression model path, analysis of deviance table and the row with the best fitting model, using AIC as criterion, is highlighted in gray

Evidence for Research Question 1

The initial hypothesis regarding the first question was that a brain-training game would help participants improve their rule application component of problem-solving skill better than a roleplaying game after 20 hours of gameplay for several reasons. One reason is that the rules are more explicit during brain-training gameplay and because of claims made by CogniFit that brain-training gameplay will improve its users’ brain fitness or ability to rely on more than one problem-solving strategy. While both games require players to apply rules to solve problems, only CogniFit markets its product as a tool that can help users to solve problems in their daily lives ( CogniFit, 2019 ). This claim also suggests that brain-training gameplay can help users transfer skills learned in-game to novel problem-solving scenarios in the natural world. However, the results indicated that there was no significant difference in gain scores (i.e., in Post - Pre Gain scores) in terms of TOH performance (t-test comparing gain scores: p = .746) between the two gaming conditions (i.e., Warcraft and CogniFit ), though both groups improved from baseline to post-testing assessment, likely attributable to practice effects (see Figure 5 ). Overall, the results contradicted our initial hypothesis for Research Question 1; implications are discussed next.

Average number of moves in the Tower of Hanoi task across (up to 3) sessions per person, per timepoint. The left panel represents scores for the CogniFit (COG) group, and the right panel represents scores for the Warcraft (WOW) group.

Implications of Results for Research Question 1

Solving problems in an immersive game like Warcraft provided players with repeated practice of applying rules and using tools to find creative solutions to similar but varied problems. As players reflected on their choices, they learned how to use the tools by analyzing givens and constraints in unison to achieve maximum character performance and develop optimal solutions to general problems. CogniFit players did not experience immersive gameplay, but instead repeated problem-solving scenarios that were varied but required fewer tools and resources to be solved. Once CogniFit players knew how to use the paddle and the ball in unison, the only additional resources to use during gameplay were power-ups, bonus bricks, and traps. Roleplaying gameplay required players to solve problems using additional tools and resources efficiently which was a more complex task than using the ball and paddle during brain-training gameplay. Strategizing when and how to apply rules through varied but different problem scenarios with multiple tools and resources through immersive gameplay was beneficial for Warcraft participants. Moreover, players in Warcraft could receive feedback with help from other players learning when and how to apply tools and resources to solve problems. CogniFit players received feedback at the end of each level with an overall score and corrected mistakes through trial and error without additional support.

evidence for Research Question 2

The initial hypothesis regarding the second question was that training on an immersive, collaborative roleplaying video game for 20 hours would engender transfer of problem-solving skills to novel problem-solving scenarios on the PISA Problem Solving Test better than a brain-training video game. One reason is that research on MMORPGs including Warcraft indicates that players co-constructed knowledge by challenging and supporting novel ideas to in-game problem-solving scenarios through online discussion forums as well as discovering optimal solutions to in-game problems by combining multiple abilities and resources available to players ( Chinn & Malhotra, 2002 ; Steinkuehler & Chmiel, 2006 ). Efficiently using tools and resources is a component of problem solving and is central to the roleplaying gameplay experience ( Shute & Wang, 2016 ).

However, the results indicated that after 20 hours of gameplay of Warcraft or CogniFit there was no improved performance on the PISA (i.e., comparing PISA Gain Scores; p = .748). Overall, the mean scores for Warcraft participants were slightly better than CogniFit participants on the isomorphic versions of the PISA Problem Solving pretest and posttest - indicating baseline differences between the two groups in terms of performance. Overall, there were no significant differences found between roleplaying and brain-training gameplay on transfer of problem-solving skills (see Figure 6 ). The implications for the results from research question 2 are discussed next.

PISA Scores before and after the intervention. The left panel represents scores for the COG group, and the right panel represents scores for the WOW group.

Implications of Results for Research Question 2

Given that both video game training and “brain-training” did not significantly improve problem-solving skills has several implications. The gameplay behaviors exhibited by players in each condition were aligned with the problem-solving processes on the PISA Problem Solving Test. However, possible reasons for lack of transfer in this study in addition to small sample size include (a) collaborative, immersive roleplaying gameplay may help promote problem-solving skills related to in-game problem solving scenarios but not necessarily to improved performance on external problem-solving assessments, and (b) problem-solving during Warcraft gameplay may be too domain specific to transfer to novel problem-solving scenarios on the PISA Problem Solving Test.

The misalignment between the problem-solving domains of Warcraft and the PISA Problem Solving Test could have hindered the possibility of finding a transfer effect. As an example, Warcraft players must learn how to navigate an immersive environment, use complex tools efficiently and effectively to solve problems during gameplay and interact with both the environment and other characters to solve problems. However, solving problems on the PISA Problem Solving Test is not an immersive experience. It was also a solitary activity; participants did not collaborate or interact with each other while taking the test. The OECD designed the PISA Problem Solving Test to cover more general problem-solving skills to complement domain-specific skills ( Greiff et al., 2014 ). Selecting a problem-solving assessment which is embedded within an immersive environment that requires players to engage in collaborative problem-solving processes (i.e. experienced in video gameplay) using tools and resources efficiently could have been a more viable assessment to measure transfer of problem-solving skills in this study. Further research is still warranted to determine if video gameplay can promote transfer of problem-solving skills to novel scenarios. The limitations of this research study are addressed in the next section.

Limitations

Given time and resource constraints, the sample size of this study is small and lacks statistical significance to make claims regarding the general population. With more available resources, recruitment would have likely continued for an additional semester to raise the sample size for the study. Students that did not complete the study cited time constraints as the main reason they were unable to fulfill the 20 hours of video gameplay requirement. The optimal time to run the study would have been during Fall and Spring semesters instead of Spring and Summer. In Fall and Spring, more students would have been available for recruitment as well as increased scheduling flexibility and time to complete the intervention during the academic year for the participants. Given that the authors monitored participants during video gameplay in case any problems arose, there may have been expectancy effects that impacted participants. For example, participants’ gameplay experiences may have been negatively or positively affected when being monitored. The potential for participants to alter their behavior simply because they are being studied is known as the Hawthorne Effect ( Benedetti, Carlino & Piedimonte, 2016 ). In addition, the inclusion of a more immersive assessment that measures problem-solving skill transfer could have led to improved outcomes when compared to a more traditional assessment like the PISA Problem-Solving Test (2003).

Future Implications

The main goal of this study was to examine the impact of two distinct types of video gameplay; role playing ( Warcraft ) and brain-training ( CogniFit ) on problem-solving skills for undergraduates. Specifically, if video gameplay can improve the rule application component of problem solving and whether problem solving during gameplay transferred to novel problem-solving scenarios. This study addressed some of the methodological shortcomings found in previous video game training and transfer studies that failed to report recruitment methods, define study variables, and provide an active control group in which participants could expect receive equal improvement from competencies ( Baniqued et al., 2013 ; Boot et al., 2013 ). As a result, possible placebo effects are likely mitigated in this experiment improving upon methodological pitfalls affecting other video game training studies ( Anderson et al., 2010 ; Ferguson & Kilburn, 2009 ).

The results from this study suggest that neither a commercially available video game ( Warcraft ) or a commercially available “brain-training” package ( CogniFit ) resulted in improvements in the rule-based component of problem solving (as assessed by the TOH puzzle). Moreover, aside from a lack of improvement in the rule-based component, 20-hours of training did not promote transfer of problem-solving skills to novel scenarios (as assessed by the PISA Problem Solving Task), which is consistent with similar research findings on cognitive training and transfer ( Souders et al., 2017 ). Sensitivity analyses conducted found evidence for practice effects in gain scores, illustrating that rather than improvement due to the training packages, improvement seems related to multiple, closely spaced assessments. Future research can complement this study by increasing the sample size and testing similar immersive well-designed video games on participant knowledge, skills, and abilities, in addition to directly cuing participants to be aware of the strategies (i.e., perceptual and cognitive strategies) they might carry with them from the digital world to the real-world.

Acknowledgment

Nelson Roque was supported by National Institute on Aging Grant T32 AG049676 to The Pennsylvania State University.

Benjamin Emihovich is an Assistant Professor of Educational Technology in the Education Department at the University of Michigan-Flint and is the program faculty coordinator for the online Educational Technology (M.A.) program. He currently teaches undergraduate and graduate students in the areas of Instructional Design and Technology as well as curriculum and instruction. His research area focuses on the following; game-based learning, assessments for learning in immersive environments, and emerging learning technologies.

Nelson A. Roque is a NIA T32 Postdoctoral Fellow, at Penn State’s Center for Healthy Aging. Nelson earned his Ph.D. in Cognitive Psychology from Florida State University in 2018. Nelson has a strong background in visual attention, focusing on how to reliably measure it, how it relates to individual difference factors (e.g., age, sleep) and translating insights from theoretical work in visual attention to applied contexts (e.g. medication errors).

Justin Mason is a Postdoctoral Associate in Rehabilitation Science at the University of Florida. His research interests include interventions suitable for mitigating age-related cognitive and physical decline in older adults. Additionally, he’s interested in factors that influence older adults’ adoption and acceptance of emerging technologies.

Contributor Information

Benjamin Emihovich, University of Michigan - Flint, Flint, USA.

Nelson Roque, Pennsylvania State University, State College, USA.

Justin Mason, University of Florida, Gainesville, USA.

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Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

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