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.

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?

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.

    Studies by PERTS 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: Use specific examples or cases that exemplify an abstract principle and reveal the future conditions to which it also applies. 

    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.

Kolodner, J. L. (1997). Educational implications of analogy: A view from case-based reasoning. American psychologist, 52(1), 57. [Online Reference]

Kolodner, J. L. (Ed.). (1993). Case-based learning (Vol. 10, No. 3). Springer. [Google Books Preview]

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.

Worked Example Effect

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.

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.

Richland, L.E., Zur, O., & Holyoak, K. (2007). Cognitive supports for Analogies in the mathematics classroom. Science, 316, 1128-1129. (online PDF) 

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

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.

Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129-184. [Online PDF]

Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design into practice. The journal of the learning sciences, 12(4), 495-547. [online reference]

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.

Testing & 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.

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

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