Meta-cognitive strategy instruction in intelligent tutoring systems: How, when, and why

Certain learners are less sensitive to learning environments and can always learn, while others are more sensitive to variations in learning environments and may fail to learn (Cronbach & Snow, 1977). We refer to the former as high learners and the latter as low learners. One important goal of any learning environment is to bring students up to the same level of mastery. We showed that an intelligent tutoring system (ITS) teaching a domain-independent problem-solving strategy indeed closed the gap between high and low learners, not only in the domain where it was taught (probability) but also in a second domain where it was not taught (physics). The strategy includes two main components: one is solving problems via backward chaining (BC) from goals to givens, called the BC strategy, and the other is drawing students' attention to the characteristics of each individual domain principle, called the principle-emphasis skill. Evidence suggests that the low learners transferred the principle-emphasis skill to physics while the high learners seemingly already had such skill and thus mainly transferred the other skill, the BC strategy. Surprisingly, the low learners learned just as effectively as the high learners in physics. We concluded that the effective element of transfer seemed not to be the BC strategy, but the principle-emphasis skill.