摘要
随着教育事业和数字技术的蓬勃发展,高校内大量学生学习数据开始以电子形式进行存储。预测学生在未来学习中的表现(如成绩)成为一个重要课题。在现有的学生成绩预测研究中,大多数忽略了学生历史习题之间隐含的关联性。因此,该文提出了基于知识追踪和知识网络的学生成绩预测方法,其主要思想是融合知识追踪和习题对应知识点的知识网络实现成绩预测。该方法将每个学生所做习题对应的知识点以及前驱知识点组成的知识网络作为输入之一,并在知识网络中标注出掌握程度大于给定阈值的知识点;结合LSTM神经网络,通过学生学习记录融合注意力机制来追踪学生知识水平,最终预测学生在未来做题中的表现。在公开数据集上的实验结果表明,该模型在Assistment2009数据集上较DKT、DKT+、DKVMN、SAKT、MFKT、MSKT分别提高了2.3%,2.2%,3.3%,3.4%,0.41%,2.0%,在Assistment2015数据集上分别提高了9.0%,9.4%,9.7%,9.3%,8.1%,8.5%,具有较好的模型精度。
With the boom in education and digital technology,large amounts of student learning data are beginning to be stored in electronic form within universities.Predicting student performance(e.g.,grades)in future studies has become an important topic.In reported research on student performance prediction,most of them overlook the implicit correlation among students'historical exercises.Therefore,we propose a method to predict student performance based on knowledge networks and knowledge tracking.The main idea is to integrate the knowledge tracking and the knowledge network formed by the association between knowledge points of exercise.The proposed method takes the knowledge network composed of exercise-specific knowledge points and their precursor knowledge points as one of the inputs and masks the knowledge points with mastery level greater than a given threshold in the knowledge network.Combined with LSTM,students’knowledge level is tracked through the integration of learning records and attention mechanisms,ultimately predicting students’performance in future problem-solving.The experimental results on public datasets show that the model proposed improves by 2.3%,2.2%,3.3%,3.4%,0.41%and 2.0%on the Assistment2009 dataset,and by 9.0%,9.4%,9.7%,9.3%,8.1%and 8.5%on the Assistment2015 dataset,respectively,compared to DKT,DKT+,DKVMN,SAKT,MFKT and MSKT,has better model accuracy.
作者
周铖
郝国生
张谢华
杨晓菡
祝义
ZHOU Cheng;HAO Guo-sheng;ZHANG Xie-hua;YANG Xiao-han;ZHU Yi(School of Computer Science and Technology,Jiangsu Normal University,Xuzhou 221116,China;Jiangsu Wisdom-driven Research Institute Co.,Ltd.,Xuzhou 221000,China)
出处
《计算机技术与发展》
2024年第8期202-208,共7页
Computer Technology and Development
基金
国家自然科学基金(62277030,62077029)。
关键词
知识追踪
知识网络
成绩预测
注意力机制
长短期记忆网络
knowledge tracking
knowledge networks
performance prediction
attention mechanism
long short-term memory(LSTM)
