Improving Knowledge Learning Through Modelling Students' Practice-Based Cognitive Processes
Improving Knowledge Learning Through Modelling Students' Practice-Based Cognitive Processes
复制标题
通过对学生基于实践的认知过程进行建模来改善知识学习
DOI：
10.1007/s12559-023-10201-z
复制
发表时间：
2023
影响因子：
5.4
通讯作者：
Gao H
中科院分区：
文献类型：
--
作者：
Gao H
实践是人类和动物进行认知活动的基本手段。智能辅导系统是学习者在学习过程中认知过程建模和优化学习策略的重要组成部分，为研究学生基于实践的认知过程提供了一个很好的平台。在相关研究中，认知过程的建模方法已经显示出值得称道的表现。此外，研究人员还扩展了他们的研究，使用决策理论方法，如部分可观察的马尔可夫决策过程（POMDP），通过模拟学生的认知过程来诱导学习策略。然而，现有的研究主要集中在宏观层面的教学行为建模，而不是学生在复杂的认知领域中做出的具体实践选择。在本文中，我们采用POMDP模型来表示学生在认知任务中的表现与他/她的认知状态之间的关系。这样，我们可以在诱导学习策略的同时预测他/她的表现。更具体地说，我们在智能辅导系统中关注学生实时学习活动中的问题选择。为了解决复杂认知领域建模的挑战，我们利用问题类型来自动化参数学习，随后使用信息熵技术来改进POMDP中的学习策略。我们在两个现实世界的知识概念学习领域进行实验。实验结果表明，新模型诱导的学习策略的性能优于其他学习策略。此外，新模型在预测学生成绩方面具有良好的信度。本文利用智能辅导系统作为研究平台，解决基于实践的复杂结构认知过程的建模和策略归纳挑战，旨在有效地辅导学生。我们的工作提供了一种新的方法来预测学生的表现以及个性化他们的学习策略。
Practice is an essential means by which humans and animals engage in cognitive activities. Intelligent tutoring systems, with a crucial component of modelling learners’ cognitive processes during learning and optimizing their learning strategies, offer an excellent platform to investigate students’ practice-based cognitive processes. In related studies, modelling methods for cognitive processes have demonstrated commendable performance. Furthermore, researchers have extended their investigations using decision-theoretic approaches, such as a partially observable Markov decision process (POMDP), to induce learning strategies by modelling the students’ cognitive processes. However, the existing research has primarily centered around the modelling of macro-level instructional behaviors rather than the specific practice selection made by the students within the intricate realms of cognitive domains. In this paper, we adapt the POMDP model to represent relations between the student’s performance on cognitive tasks and his/her cognitive states. By doing so, we can predict his/her performance while inducing learning strategies. More specifically, we focus on question selection during the student’s real-time learning activities in an intelligent tutoring system. To address the challenges on modelling complex cognitive domains, we exploit the question types to automate parameter learning and subsequently employ information entropy techniques to refine learning strategies in the POMDP. We conduct the experiments in two real-world knowledge concept learning domains. The experimental results show that the performance of the learning strategies induced by our new model is superior to that of other learning strategies. Moreover, the new model has good reliability in predicting the student’s performance. Utilizing an intelligent tutoring system as the research platform, this article addresses the modelling and strategy induction challenges of practice-based cognitive processes with intricate structures, aiming to tutor students effectively. Our work provides a new approach of predicting the students’ performance as well as personalizing their learning strategies.