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Cognitive and metacognitive strategy had demonstrated a significant role inself-regulated learning (SRL), and an appropriate use of strategies isbeneficial to effective learning or question-solving tasks during ahuman-computer interaction process. This paper proposes a novel methodcombining Knowledge Map (KM) based data mining technique with Thinking Map (TM)to detect learner's cognitive and metacognitive strategy in thequestion-solving scenario. In particular, a graph-based mining algorithm isdesigned to facilitate our proposed method, which can automatically mapcognitive strategy to metacognitive strategy with raising abstraction level,and make the cognitive and metacognitive process viewable, which acts like areverse engineering engine to explain how a learner thinks when solving aquestion. Additionally, we develop an online learning environment system forparticipants to learn and record their behaviors. To corroborate theeffectiveness of our approach and algorithm, we conduct experiments recruiting173 postgraduate and undergraduate students, and they were asked to complete aquestion-solving task, such as "What are similarities and differences betweenarray and pointer?" from "The C Programming Language" course and "What aresimilarities and differences between packet switching and circuit switching?"from "Computer Network Principle" course. The mined strategies patterns resultsare encouraging and supported well our proposed method.

Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving