Prospective Adaptation

One of the many goals of an educator is to prepare their students to adapt what they have learned in new situations. There can be two types of adaptation: fault-driven adaptation (which are reactions to a difficult situation), and prospective adaptation (which are proactive reformulations of one's knowledge or environment prior to encountering a new problem of situation). Martin and Schwartz (2009) show that graduate students uniformly make prospective adaptations to create meaningful representations of available information much more often than undergraduate students before diagnosing a problem, even though this may cost them some start up time. Undergraduate students who do not have continuous access to reference material tend to create more meaningful representations than students who have continuous access. The long term benefit for the graduate students in creating meaningful representation through prospective adaptation is that they complete a new diagnostic task much quicker than others with no meaningful representation.

In Computer Science, as in many other Science disciplines, students are not usually given the time or opportunity to step back, reflect, and retool one's knowledge. In first year programming, students are taught to solve computing problems through a systematic and methodical way while they may not understand why "hacking" is not suitable. But rather than short circuiting this process of prospective adaptation which the students should work through themselves, perhaps the students should be allowed to hack their code, and then asked to step back to rethink what other ways of problem solving is more appropriate when given a more complex problem. Instead, most often, algorithmic or procedural formulations are provided and students often try to memorize these solutions, hoping that these are sufficient for any new problems they will encounter. A learning goal should be included in each course where students are expected to engage in prospective adaptation to create new representations and ways of integrating new ideas with their knowledge base. This can be in the form of creating concept maps, writing up summaries of new knowledge and its connections to other areas (e.g. through a blog), designing new procedures or methods in solving problems, or engaging in invention activities (which encourage risk taking at little cost).

Reference:

Martin, L. and Schwartz, D. (2009). Prospective Adaptation in the Use of External Representations. Cognition and Instruction, 27(04), pp 370 - 400.

Student Self-Explanation

Student self-explanation of material they just read has been shown to be effective in producing robust learning gains in a number of disciplines. However, past research results have not been clear whether performance gain is due to student simply paying attention to explanation generated by the instructors, or explanation generated by the students themselves. One research has shown that explanation is more effective when the students generate it rather than simply paying attention to instructor generated explanations (Brown and Kane, 1988), while in another case, the reverse is true (Lovett, 1992). Most recently, Hausmann and Vanlehn (2007) show that generating self-explanation while students attempted solving problems and studying examples is more effective in normal as well as robust learning (which means knowledge is retained over a significant period of time and demonstrated in far transfer of problem solving) than students who comprehended and paraphrased explanations generated by the instructors.

Self-explanation, coupled with learning by examples, can be very effective in student learning. Learning by examples has a lower cognitive load than learning by doing or solving problems, based on cognitive load theory. Thus comparing students who learn by doing a number of questions with those who learn by working through a number of examples, the cognitive load in the latter is much lower, and this affords the students the capacity to come up with general solution principles through self-explanation to improve their effectiveness in learning.

A related theme is that students who self-monitor their learning and comprehension in addition to self-explain the material they learned are better problem solvers than those who don't. By self-monitoring, this means that the students keep track of what they know and what they don't know, what are the parameters and data provided by the problems they are trying to solve, what needs to be solved, how the problems relate to the examples they have worked through having specific goals such as looking for solution methods rather than equations, formulas, similar contexts, etc.

References:

Brown, A.L. and Kane, M.J. (1988). Preschool Children Can Learn to Transfer: Learning to Learn and Learning from example. Cognitive Psychology. 20(4), pp 493 - 523.

Hausmann, R.G.M. and Vanlehn, K. (2007). Explaining Self-Explaining: A Contrast Between Content and Generation. In R. Luckin, K.R. Koedinger, and J. Greer (Eds). Proceedings of Artificial Intelligence in Education (2007). Amsterdam, The Netherlands: IOS Press.

Lovett, M.C. (1992). Learning by Problem Solving versus by Examples: The Benefits of Generating and Receiving Information. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Erlbaum, pp 956 - 961.