Identification of Different Patterns in Solving Collaborative Jigsaw Puzzle Tasks Using Hidden Markov Models
Qizhi Xu, Zongting Ge, Mengxiao Zhu, Ziwei Dou, Jing Wang · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · 2024
To assess learner's collaborative problem-solving (CPS) skills, it is necessary to identify their behavior patterns in completing collaborative tasks.These patterns are concealed within the collaboration and problem-solving processes, and can only be analyzed through modeling of the detailed process data.This study utilized Hidden Markov Models (HMMs) to analyze 15,767 instances of process data generated by 63 students in an online collaborative jigsaw puzzle task.With the goal of comparing low and high performing teams, we identified three hidden states in the low-scoring group, including Warm-up, Action, and Strategy Execution and Validation states, and four hidden states for the high-scoring group, including the three states for the low-scoring group plus a new Strategy Optimization state.Furthermore, the results showed that even though both groups initiated the task with the Warm-up state, the high-scoring group exhibited more complex transitions between states than the low-scoring group.