序列特征与学习过程融合的知识追踪模型

doi:10.19678/j.issn.1000-3428.0067528

摘要/Abstract

摘要： 知识追踪是人工智能技术与教育相结合的新兴领域,旨在通过学生过去完成习题的交互序列对学生的知识状态进行评估,是实现大规模个性化学习服务的关键核心技术。随着深度学习在计算机视觉、自然语言处理、推荐系统等领域的广泛应用,知识追踪领域也出现了大量基于神经网络的方法,简称深度知识追踪(DKT)模型。针对目前已有DKT模型在可解释性和准确性方面的不足,提出一种序列特征与学习过程融合的知识追踪模型SLKT,模型包括知识状态模块、序列特征模块、预测模块。知识状态模块用以模拟学生学习过程,序列特征模块捕捉学习者近期学习状况。通过序列特征和学习过程的融合,有效解决了基于知识状态建模方法无法考虑学习者近期学习状况的问题,同时提出一种带约束的动态Q矩阵表示练习和知识点之间的关系,从而更好地进行学习者学习过程建模,在确保较好可解释性的同时有效提升模型的准确性。在3个知识追踪领域公共数据集上的实验结果表明,相比DKT、动态键值记忆网络(DKVMN)、自注意力的知识追踪(SAKT)、卷积知识追踪(CKT)等深度追踪模型,SLKT模型在曲线下面积(AUC)、准确率指标评估中表现较优。

关键词: 智慧教育, 深度学习, 知识追踪, 学习过程建模, Q矩阵

Abstract: Knowledge tracing is a novel field that merges artificial intelligence and education with the aim of evaluating the knowledge state of students through an interactive sequence of exercises that they have completed in the past. This is a core technology for implementing large-scale personalized learning services. With the widespread application of deep learning in computer vision, Natural Language Processing (NLP), recommendation systems, and other fields, a significant number of neural network-based methods, known as Deep Knowledge Tracing(DKT) models, have emerged in the field of knowledge tracing. To address the shortcomings of existing DKT models in terms of interpretability and accuracy, this study proposes a knowledge tracing model called SLKT that integrates sequence features with learning processes and includes knowledge state, sequence feature, and prediction modules. The knowledge state module is used to simulate the learning process of students, and the sequence feature module captures their recent learning status. Through the integration of sequence features and the learning process, the SLKT model effectively solves the problems of knowledge state modeling methods that cannot consider learners' recent learning status. A dynamic Q matrix with constraints is also proposed to represent the relationship between exercises and knowledge points to better model the learning process of the learner. This ensures better interpretability, and effectively improves model accuracy. In this study, the superior performance of the proposed model is verified using the Area Under Curve (AUC) and accuracy metrics, comparing multiple sets of experiments using depth tracing models such as DKT, Dynamic Key-Value Memory Network (DKVMN), Self-Attentive Knowledge Tracing (SAKT), and Convolutional Knowledge Tracing (CKT) on public datasets in three knowledge tracing fields.

Key words: smart education, deep learning, knowledge tracking, learning process modeling, Q matrix

中图分类号:

TP391

李子杰, 周菊香, 韩晓瑜, 甘健侯, 鹿泽光, 王俊. 序列特征与学习过程融合的知识追踪模型[J]. 计算机工程, 2024, 50(6): 77-85.

LI Zijie, ZHOU Juxiang, HAN Xiaoyu, GAN Jianhou, LU Zeguang, WANG Jun. Knowledge Tracing Model Based on the Fusion of Sequence Features and Learning Processes[J]. Computer Engineering, 2024, 50(6): 77-85.

https://www.ecice06.com/CN/Y2024/V50/I6/77

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