Enhanced Dynamic Key-Value Memory Networks for Personalized Student Modeling and Learning Ability Classification

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huanhuan Zhang, Lei Wang, Yuxian Qu, Wei Li, Qiaoyong Jiang
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引用次数: 0

Abstract

Knowledge tracing (KT) is a technique that can be applied to predict students’ current skill mastery levels and future academic performance based on previous question-answering data. A good KT model can more accurately reflect a student’s cognitive processes and provide a more realistic assessment of skill mastery level. Currently, most KT models regard all students as a whole, while ignoring their personal differences; a few KT models attempt to personalize the modeling of students from the perspective of their learning abilities, among which a typical example is Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). However, these models have a relatively coarse-grained approach to modeling students’ learning abilities and cannot accurately capture the nonlinear relationship between students’ learning abilities and the questions they answer. To solve these problems, we propose a novel KT model named the Enhanced Dynamic Key-Value Memory Networks for Dynamic Student Classification (EnDKVMN-DSC). This model is specifically designed for personalized student modeling and learning ability classification. Specifically, first, we propose a novel Enhanced Dynamic Key-Value Memory Network (EnDKVMN) and use it to model each student’s learning ability. Second, students are classified according to their learning abilities based on the K-means algorithm. Finally, the enriched input features are constructed and passed through Gated Recurrent Unit (GRU) networks to obtain prediction results. All experiments are conducted on four real-world datasets to evaluate our proposed model, and the results show that EnDKVMN-DSC outperforms the other four state-of-the-art KT models based on DKT or DKVMN in predicting student performance.

Abstract Image

用于个性化学生建模和学习能力分类的增强型动态键值记忆网络
知识追踪(Knowledge Tracing,KT)是一种可用于根据以往的答题数据预测学生当前技能掌握水平和未来学业成绩的技术。一个好的 KT 模型可以更准确地反映学生的认知过程,并提供更真实的技能掌握水平评估。目前,大多数 KT 模型将所有学生视为一个整体,忽略了学生的个体差异;少数 KT 模型试图从学生学习能力的角度对学生进行个性化建模,其中一个典型的例子是动态学生分类深度知识追踪模型(DKT-DSC)。然而,这些模型对学生学习能力的建模粒度相对较粗,无法准确捕捉学生学习能力与所回答问题之间的非线性关系。为了解决这些问题,我们提出了一种新颖的 KT 模型,即用于动态学生分类的增强型动态键值记忆网络(EnDKVMN-DSC)。该模型专为个性化学生建模和学习能力分类而设计。具体来说,首先,我们提出了一个新颖的增强型动态键值记忆网络(EnDKVMN),并用它为每个学生的学习能力建模。其次,根据 K-means 算法对学生的学习能力进行分类。最后,构建丰富的输入特征,并通过门控递归单元(GRU)网络获得预测结果。实验结果表明,EnDKVMN-DSC 在预测学生成绩方面优于其他四种基于 DKT 或 DKVMN 的先进 KT 模型。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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