The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems
This work addresses improving learning efficiency for students in deductive domains like logic and probability, but it is incremental as it builds on existing research on strategy instruction.
The study tackled the problem of teaching students metacognitive skills to choose optimal problem-solving strategies in intelligent tutoring systems, finding that a nudge intervention helped default-strategy students catch up to skilled peers on both logic and probability tutors.
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems. It was shown that students who know how and when to use each strategy (StrTime) outperformed those who know neither and stick to the default strategy (Default). In this work, students were trained on a logic tutor that supports a default forward-chaining and a backward-chaining (BC) strategy, then a probability tutor that only supports BC. We investigated three types of interventions on teaching the Default students how and when to use which strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime students received no interventions. Overall, our results show that Nudge outperformed their Default peers and caught up with StrTime on both tutors.