.png){kind=icon}
返回
Improving Knowledge Learning Through Modelling Students' Practice-Based Cognitive Processes
.png){kind=icon}
OA
AI
DOI：10.1007/s12559-023-10201-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Cognitive model
Learning strategies induction
Partially observable Markov decision process (POMDP)
Intelligent education
期刊
IF:
4.3
论文数:
1.6K
被引数:
3.6K
机构
引用论文
A new feedback control method to stabilize fiber-optical parametric oscillators for clock extraction一种用于时钟提取的光纤光学参量振荡器稳定化的新反馈控制方法
PREAI
分享收藏A single-domain antibody fragment in complex with RNase A: non-canonical loop structures and nanomolar affinity using two CDR loops.png)
Structure
OAAI
StructureIF0
OAAI分享收藏
查看更多内容