Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
复制标题
基于 MOOC 认知诊断评估的数据驱动个性化学习路径规划
DOI：
10.3390/app12083982
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发表时间：
2022-04-01
影响因子：
2.7
通讯作者：
Lin, Qiaomin
中科院分区：
文献类型：
--
作者：
Jiang, Bo;Li, Xinya;Lin, Qiaomin
个性化学习路径旨在为异质学生提供最合适的学习顺序，从而节省学习时间，提高学习成绩。现有的构建个性化学习路径的方法大多关注学生的特征或知识结构，而忽略了学习状态的重要作用。本文提出了一种动态的个性化学习路径规划算法，根据学生的学习状态和知识点的难度，为在线学生推荐合适的知识点。该方法首先自动计算知识点的难度，构建知识点的难度模型。然后，基于学习行为和归一化考试成绩建立了动态知识掌握模型。最后生成满足学生个性化变化状态的路径。为了实现上述目标，提出了一种自动计算知识点难度的新方法。此外，本研究所提出的个人化学习路径规划方法并不局限于特定的课程。为了评估该方法，我们使用了一系列的方法来验证个性化路径对学生学习的影响。实验结果表明，该算法能够有效地生成个性化学习路径。实验结果表明，该算法提出的个性化路径可以提高有效行为率、课程完成率和学习效率。研究结果还表明，基于学生状态的个性化学习路径有助于学生掌握知识。
Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students' characteristics or knowledge structure, while ignoring the critical roles of learning states. This study describes a dynamic personalized learning path planning algorithm to recommend appropriate knowledge points for online students based on their learning states and the difficulty of each knowledge point. The proposed method first calculates the difficulty of knowledge points automatically and constructs a knowledge difficulty model. Then, a dynamic knowledge mastery model is built based on learning behavior and normalized test scores. Finally, a path that satisfies students' personalized changing states is generated. To achieve the aforementioned goal, a novel method that calculates the difficulty of knowledge points automatically is proposed. Moreover, the personalized learning path planning method proposed in this research is not limited to a particular course. To evaluate the method, we use a series of approaches to verify the impact of the personalized path on student learning. The experimental results demonstrate that the proposed algorithm can effectively generate personalized learning paths. Results demonstrate that the personalized path proposed by the algorithm can improve effective behavior rates, course completion rates and learning efficiency. Results also show that the personalized learning paths based on student states would help students to master knowledge.