Affiliation:
1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
2. Institute of Sensing Technology, Gansu Academy of Sciences, Lanzhou, China
Abstract
Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference8 articles.
1. Knowledge tracing: Modeling the acquisition of procedural knowledge;Albert Corbett;User Modeling and User-Adapted Interaction,1994
2. Knowledge Tracing: A Survey;Ghodai Abdelrahman;ACM Computing Surveys,2023
3. Memory: A contribution to experimental psychology;Hermann Ebbinghaus;Ann Neurosci,2003
4. Deep learning;LeCun;Nature,2015
5. The form of the forgetting curve and the fate of memories;Lee Averell;J Math Psychol,2011