基于学生知识追踪的多指标习题推荐算法

诸葛斌; 尹正虎; 斯文学; 颜蕾; 董黎刚; 蒋献

doi:10.11959/j.issn.1000-0801.2022234

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基于学生知识追踪的多指标习题推荐算法

研究与开发 | 更新时间：2024-06-05

- 基于学生知识追踪的多指标习题推荐算法
- Student knowledge tracking based multi-indicator exercise recommendation algorithm
- 电信科学 2022年38卷第9期页码：129-143
- 作者机构：
- 作者简介：
  
  [ "诸葛斌（1976- ），男，博士，浙江工商大学信息与电子工程学院教授，主要研究方向为网络和通信技术、互联网技术和网络安全" ]
  [ "尹正虎（1997- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为在线教育、自然语言处理和个性化推荐" ]
  [ "斯文学（1996- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为在线教育、数据分析和个性化推荐" ]
  [ "颜蕾（1996- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为教育数据挖掘、深度学习和机器学习" ]
  [ "董黎刚（1972- ），男，博士，浙江工商大学信息与电子工程学院教授，IEEE 和IEEE-CS成员，中国电子学会高级会员，浙江计算机学会理事，主要研究方向为智能网络、在线教育" ]
  [ "蒋献（1988- ），男，浙江工商大学信息与电子工程学院讲师、实验员，主要研究方向为在线教育" ]
- 基金信息：
  
  浙江省重点研发计划项目;Zhejiang Province Key Research and Devel-opment Program(2021C01036);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY18F010006);浙江省新型网络标准与应用技术重点实验室项目(2013E10012);浙江工商大学高等教育研究课题;Higher Education Research Project of Zhejiang Gongshang University(Xgy21012)
- DOI：10.11959/j.issn.1000-0801.2022234
  中图分类号： TP301
- 网络出版日期：2022-09，
  
  纸质出版日期：2022-09-20
- 稿件说明：
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诸葛斌, 尹正虎, 斯文学, 等. 基于学生知识追踪的多指标习题推荐算法[J]. 电信科学, 2022,38(9):129-143.

Bin ZHUGE, Zhenghu YIN, Wenxue SI, et al. Student knowledge tracking based multi-indicator exercise recommendation algorithm[J]. Telecommunications science, 2022, 38(9): 129-143.
诸葛斌, 尹正虎, 斯文学, 等. 基于学生知识追踪的多指标习题推荐算法[J]. 电信科学, 2022,38(9):129-143. DOI： 10.11959/j.issn.1000-0801.2022234.

Bin ZHUGE, Zhenghu YIN, Wenxue SI, et al. Student knowledge tracking based multi-indicator exercise recommendation algorithm[J]. Telecommunications science, 2022, 38(9): 129-143. DOI： 10.11959/j.issn.1000-0801.2022234.

摘要

个性化习题推荐是教育信息化时代的重要课题，传统的习题推荐算法忽略了学生在学习过程中的遗忘规律，未能充分挖掘学生的知识掌握水平和相似学生的共性特征，推荐习题的评价指标单一，推荐习题的新颖度和多样性不足，不能合理地促进学生对新知识的学习或帮助学生查缺补漏。针对上述缺陷，提出一种基于学生知识追踪的多指标习题推荐方法，该方法分为习题初筛和再过滤两个模块，围绕习题推荐的新颖度、难度以及多样性 3 个指标展开研究，首先构造了一个结合学生遗忘规律的知识概率预测（student forgetting behavior based knowledge concept coverage prediction，SF-KCCP）模型，保证推荐习题的新颖性；接着基于动态键值的知识追踪（dynamic key-value memory networks for knowledge tracing，DKVMN）模型精准挖掘学生的知识概念掌握水平，以保证推荐合适难度的习题；最后将基于用户的协同过滤（user-based collaborative filtering，UserCF）算法融入再过滤模块，利用学生群体之间的相似性实现推荐结果的多样性。通过大量的实验证明，所提方法比一些现有的基线模型取得了更好的性能。

Abstract

Personalized exercise recommendation was an important topic in the era of education informatization

the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm

which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students

insufficient

could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects

a multi-index exercise recommendation method based on student knowledge tracking was proposed

which was divided into two modules: preliminary screening and re-filtering of exercises

focusing on the novelty

difficulty and diversity of exercise recommendation.Firstly

a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then

students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally

the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module

and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.

关键词

Keywords

references

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