Using Cognitive Science Research on Learning to Improve Education

As of Nov 14 2012, this document is no longer updated – the new version is available at this webpage:

www.josephjaywilliams.com/education

Introductions & Overviews

Meta-discussion of Learning & Education Research

Articles on specific topics

Transfer:  Explicitly think about the situations and problems that the knowledge being gained has to transfer and generalize to.

Mindset: Teach a growth mindset of intelligence to boost motivation and learning.

Specific examples linked to abstract generalizations. Communicate abstract principles as they apply to particular examples and cases, and consider what specific features of future situations will remind people of a principle.

Analogy: Use analogies, metaphors and models that people already understand to help them interpret new abstract concepts.

Problem-based learning: Conveying knowledge in the context of solving problems.

Explanation: Asking questions and soliciting explanations from learners.

Retrieval Practice: Using quizzing, testing and retrieval practice to maximize transfer.

Cognitive Tutors: Using computer programs to model students’ thinking and provide adaptive feedback

What are powerful ways to help people learn and to produce lasting change in their behavior and habits?

What principles and techniques lead to excellent educational programs?

Every time we teach or learn, we are making decisions and taking actions that can be understood as implicitly (often automatically) answering these two questions. Social science research is unlikely to provide definitive laws and generalizations, whether it relies on experiments (psychology & education), qualitative observations (education & sociology), or defining concepts and reasoning logically about theories (philosophy). But we can use the knowledge gained from social sciences to substantially improve how we understand and reason about the knowledge and processes that underlie learning.

For any topic we can imagine, there is relevant information on it, even if dredging it up requires a collaboration between an expert, their favorite search engine, and Google Scholar. Scientific research in Cognitive Science and Education has produced literally thousands of journal papers, edited volumes, analyses of teaching techniques, evaluations of technology, mass media books, and practical guides. Since exposure to research can range from non-existent to overwhelming, this document contains a selection of reading material to provide a condensed answer to those original two questions.

While these choices obviously can’t be definitive or comprehensive, they represent principles that play a central theoretical role in understanding learning and whose value has been evident across many practical contexts. They have recurred repeatedly in my experience trawling through thousands of papers and books on human learning and reasoning, considering practical educational implications while in hundreds of lectures, discussions, and meetings, and examining at least a hundred different e-learning and online education programs and multimedia. I wish I had first read each of these when I started to formally study learning seven years ago, and would recommend them to anyone who has limited time.

Introductions & Overviews

Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing Instruction and Study to Improve Student Learning. IES Practice Guide. NCER 2007-2004. National Center for Education Research. (online pdf)

Clark, R. C., & Mayer, R. E. (2011). e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning (3rd ed.). Pfeiffer. (Google Books)

Michael W Bridges. (2010). How Learning Works: Seven Research-Based Principles for Smart Teaching (Jossey-Bass Higher and Adult Education).  (Amazon)

Willingham, D. T. (2010). Why Don't Students Like School. Jossey-Bass. (Amazon)

Meta-discussion of Learning & Education Research

Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2010).  The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Submitted for peer review. [PDF]

APS Observer - Toward the Tipping Point - Grover Whitehurst, IES

APS Observer, How We Learn, Hal Pashler

Schoenfeld, A. Improving Educational Research: Toward a More Useful, More Influential, and Better-Funded Enterprise (online pdf)

Articles on specific topics

Transfer:  Explicitly think about the situations and problems that the knowledge being gained has to transfer and generalize to.

Intuitively, we often seem to think of learning as adding information to a bucket – facts and concepts go in, and get retrieved later. One of the substantive insights of research in memory, high-level cognition, and education is to instead use a framework in which the goal of education is to produce transfer of knowledge. How do you get people to process and encode knowledge so that it is spontaneously transferred – retrieved and applied in relevant future contexts?

Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61-100.  (online PDF)        

Mestre, J. P. (2005). Transfer of learning from a modern multidisciplinary perspective. Information Age Pub Incorporated. (Google Books) (Hammer et al chapter) (Schwartz et al chap.)

Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. doi:10.1037//0033-2909.128.4.612 (online PDF)

Mindset: Teach a growth mindset of intelligence to boost motivation and learning.

One of the most effective ways to improve learning may not stem from a direct focus on learning, but instead by changing people's implicit underlying assumptions about whether intelligence is a quality that is fixed, or a quality that is malleable and can grow.

It may seem obvious – or at least most would agree once it's pointed out to them – that people will be more likely to learn if they think they can succeed at it. But many interventions to change behavior or improve learning don't target the specific belief about the nature of intelligence, even if they might offer encouragement.

The key value of the work on implicit theories is explicating what form this knowledge takes, how it can be changed, and what the impact is.

Dweck, C.S. (2008). Can personality be changed? The role of beliefs in personality and change. Current Directions in Psychological Science, 17, 391-394. (online pdf)

Yeager, D. S., & Walton, G. M. (2011). Social-Psychological Interventions in Education: They're Not Magic. Review of Educational Research, 81(2), 267–301. doi:10.3102/0034654311405999

www.perts.net

Additional references

Some studies have revealed extremely impressive findings: two 45 minute classes teaching middle school students and undergraduates that intelligence is malleable (rather than fixed) can improve their actual grades. Very few interventions impact such an important and broad measure, despite using far more time and resources. They are also often restricted to just one content area or set of skills.

Specific examples linked to abstract generalizations. Communicate abstract principles as they apply to particular examples and cases, and consider what specific features of future situations will remind people of a principle.

Case-based reasoning is a topic that gets at issues of how people learn from studying cases that manifest abstract principles, and are then generalized in encountering new situations and new problems. It's an interesting context to think about issues like how people are able to use abstract generalizations, by linking them to specific features of cases or conditions in which they are relevant.

Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3-34.

Benjamin, A. S. & Ross, B. H. (in press).  The causes and consequences of reminding.  In A. S. Benjamin (Ed.), Successful remembering and successful forgetting: A Festschrift in honor of Robert A. Bjork.  New York, NY: Psychology Press.

Comparison: Help learners grasp or construct new abstract principles by comparison of specific examples of the generalization.

Engaging in comparison or 'analogical encoding' (e.g. figuring out how an atom and the solar system are similar and different) has also been found to be an effective way of discovering abstract relationships. Even if you haven't thought about comparison much, you can probably find a way to link it to a learning topic or use it beneficially to improve learning – it’s a good examples of a domain-general learning strategy.

Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393-408. doi: 10.1037/0022-0663.95.2.393.

Analogy: Use analogies, metaphors and models that people already understand to help them interpret new abstract concepts.

Many acts of interpreting and representing a new situation or educational materials (e.g. think of learning math) can be interpreted as involving analogies to past experience. Providing analogies that appropriate relate new concepts and principles to a knowledge structures that a learner already possesses are extremely helpful for deep and lasting learning, although we often instead seem to communicate abstract concepts primarily through words.

Gentner, D. & Smith, L. (2012). Analogical reasoning. In V. S. Ramachandran (Ed.) Encyclopedia of Human Behavior (2nd Ed.). pp. 130-136. Oxford, UK: Elsevier.

Jee, B. D., Uttal, D. H., Gentner, D., Manduca, C., Shipley, T. F., Tikoff, B., ...Sageman, B. (2010). Commentary: Analogical thinking in geoscience education. Journal of Geoscience Education.

Problem-based learning: Conveying knowledge in the context of solving problems.

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235-266.

Explanation: Asking questions and soliciting explanations from learners.

Peer instruction involves making lectures interactive by pausing for students to explain concepts to each other or solve problems, as opposed to lecturing continuously, and is very popular now in the concept of the "flipped lecture". "Reciprocal Teaching” is a learning technique that teaches students about  what is needed to understand written content deeply, by alternating between learners trying to teach others as well as being taught. Both topics tie in well with theoretical issues in cognitive science, development, and education – like how generating explanations helps people learn.

Farewell, Lecture? Eric Mazur, Science. (2009)

Peer Instruction: Ten Years of Experience and Results. (2001)

Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and instruction, 1(2), 117–175. (online pdf)

Retrieval Practice: Using quizzing, testing and retrieval practice to maximize transfer.

There is an excellent literature on "testing effects" and the benefits of "retrieval practice". These studies demonstrate that testing people's knowledge of study materials can enhance learning more than additional time spent studying. The empirical work has focused on recall from memory, but you can also consider "tests" more broadly in a way that might be relevant to you: Answering any kind of question, doing a writing exercise, generating explanations, or solving problems.

Karpicke, J. D., Blunt, J. R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331(6018), 772-775. (internal link)

There are more references here: http://psych.wustl.edu/memory/TELC/

Rohrer, D. (2009). The effects of spacing and mixing practice problems. Journal for Research in Mathematics Education, 40(1), 4-17. Retrieved from.

Cognitive Tutors: Using computer programs to model students’ thinking and provide adaptive feedback

Koedinger, K. R. & Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.) The Cambridge Handbook of the Learning Sciences, (pp. 61-78). Cambridge University Press. (online pdf)

Ritter S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14 (2):249-255.  (online pdf)

Aleven, V., McLaren, B. M., & Sewall, J. (2009). Scaling up programming by demonstration for intelligent tutoring systems development: An open-access website for middle-school mathematics learning. IEEE Transactions on Learning Technologies, 2(2), 64-78. (online pdf)

VanLehn, K. (2006) The behavior of tutoring systems.  International Journal of Artificial Intelligence in Education. 16, 3, 227-265. (online pdf)

A (strange but effective) mnemonic to remember these principles/tools and apply them to any educational example:

T Transfer

S Specific

C Comparison

A Analogy

M Mindset

P Problem-based learning

E Explanation

R Retrieval Practice

C Cognitive Tutors