网络学习者在比较问题解决中的认知和元认知策略模型
Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving
摘要：
认知策略和元认知策略在自我调节学习中具有重要作用，在人机交互过程中，策略的合理使用有助于有效地完成学习任务或问题解决任务。本文提出了一种新的方法，结合知识地图（KM）的数据采掘技术与思维地图（TM）检测学习者的认知和元认知策略的问题解决的情况。特别地，设计了一个基于图的挖掘算法，该算法能够自动地将认知策略映射为元认知策略，提高抽象层次，并使认知和元认知过程可视化，就像一个逆向工程引擎来解释学习者在解决问题时的思维方式。此外，我们开发了一个在线学习环境系统，供参与者学习和记录他们的行为。为了证实我们的方法和算法的有效性，我们进行了实验，招募了173名研究生和本科生，他们被要求完成一个问题解决任务，如“数组和指针之间的异同是什么？“从“C程序设计语言“课程和“分组交换和电路交换之间的异同是什么？“从“计算机网络原理”课程。挖掘策略模式的结果是令人鼓舞的，很好地支持我们提出的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
