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