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Refinement is a learning process that involves modifications to knowledge components, particularly to the conditions or features under which the knowledge is retrieved and should be applied. The refinement process may add a missing relevant feature to a knowledge component (a "discrimination") or remove an irrelevant feature (a "generalization")[Add REFS to Cog Sci lit like Lewis on Explanation-based generalization or Simon on discrimination nets and EPAM].

For instance, consider a typical novice knowledge component in geometry: "If angles look equal, then they are equal" (example from Aleven & Koedinger, 2002). While this knowledge component can yield correct answers, it is incorrect in general. Through learning a student may refine this knowledge component, by removing the irrelevant feature "angles look equal" and/or adding a relevant feature, like "angles opposite each other in crossing lines" or "angles that are base angles of an isosceles triangle". Such refinement leads to a new knowledge component with higher feature validity. Of course, during learning sometimes students may add an irrelevant feature condition to a knowledge component (e.g., because in an example the angles that are concluded to be equal, do look equal, a student may incorrectly induce that looking equal is a relevant feature) or may fail to add a relevant feature (e.g., not notice that the base angles in an isosceles triangle are the ones across from the equal sides).

At present we use refinement to refer to such feature changes whether they are more explicit, involving declarative or verbal knowledge components, or more implicit, involving procedural, non-verbal knowledge components like skills. Implicit instruction, like the use of examples, may lead the learner to feature refinements that they cannot verbalize. For instance, first language learners acquire the features for correct choice of articles, like "a" and "the", without being able to articulate the explicit rules for article choice. Even second language learners, as well as math and science learners, engage in such implicit feature refinement. Some instructional methods, like feature focusing, co-training, or tutoring feedback, may accelerate such implicit feature refinement. Other instructional methods, like prompted self-explanation, peer tutoring, or collaborative observing , engage students in verbalizing and reasoning about features and their relevance. Such sense making may lead students to better acquire explicit, declarative or verbal knowledge components with higher feature validity.


  • Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2).
  • Lewis (19XX). Explanation-based generalization. Cognitive Science.
  • Simon. Paper on EPAM.