基于知识追踪机的多特征融合习题推荐模型

诸葛斌; 汪盈; 肖梦凡; 颜蕾; 王冰雁; 董黎刚; 蒋献

doi:10.11959/j.issn.1000-0801.2024210

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基于知识追踪机的多特征融合习题推荐模型

研究与开发 | 更新时间：2024-11-25

- 基于知识追踪机的多特征融合习题推荐模型
- A multi-feature fusion exercise recommendation model based on knowledge tracing machines
- 电信科学 2024年40卷第9期页码：75-87
- 作者机构：
  
  浙江工商大学信息与电子工程学院，浙江杭州 310020
- 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。" ]
  [ "汪盈（2000- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为智慧教育和个性化推荐。" ]
  [ "肖梦凡（2001- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为智慧教育和个性化推荐。" ]
  [ "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。" ]
  [ "王冰雁（2000- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为智慧教育和数字水印。" ]
  [ "董黎刚（1972- ），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“尖兵”“领雁”研发攻关计划项目(2023C03202);2022年度浙江省高等教育学会高等教育研究重点立项课题(KT2022017);2023年浙江省教育厅一般科研项目(Y202353291);浙江省普通本科高校“十四五”教学改革项目(jg20220247)
- DOI：10.11959/j.issn.1000-0801.2024210
  中图分类号： TP301
- 收稿日期：2024-06-18，
  
  修回日期：2024-08-16，
  
  纸质出版日期：2024-09-20
- 稿件说明：
移动端阅览
诸葛斌,汪盈,肖梦凡等.基于知识追踪机的多特征融合习题推荐模型[J].电信科学,2024,40(09):75-87.

ZHUGE Bin,WANG Ying,XIAO Mengfan,et al.A multi-feature fusion exercise recommendation model based on knowledge tracing machines[J].Telecommunications Science,2024,40(09):75-87.
诸葛斌,汪盈,肖梦凡等.基于知识追踪机的多特征融合习题推荐模型[J].电信科学,2024,40(09):75-87. DOI： 10.11959/j.issn.1000-0801.2024210.

ZHUGE Bin,WANG Ying,XIAO Mengfan,et al.A multi-feature fusion exercise recommendation model based on knowledge tracing machines[J].Telecommunications Science,2024,40(09):75-87. DOI： 10.11959/j.issn.1000-0801.2024210.

摘要

个性化习题推荐是智慧教育个性化服务领域的重要课题，然而传统的习题推荐算法对于学生特征的研究不够彻底，对于学生知识掌握与答题行为之间的关联信息挖掘未能充分，导致推荐精准度不佳。为解决上述问题，结合知识追踪机（KTM）和基于用户的协同过滤算法，提出一种基于KTM多特征融合的习题推荐模型SKT-MFER。该模型首先构造了一个融入学生学习行为和学习能力的知识追踪模型KTM-LC，精准挖掘学生的知识掌握水平；接着设置两次筛选，先利用知识点掌握矩阵初步筛选出相似的学生，再根据认知状态相似度和习题难度相似度组合而成的综合相似度进行二次筛选，双重过滤以保障习题推荐的准确度。通过广泛的实验证明，所提方法相比于一些现有的基线模型有更好的效果。

Abstract

The subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education. Nevertheless

traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship between knowledge mastery and question-answering behaviors

leading to low recommendation accuracy. To address these issues

combining the knowledge tracing machine and the user-based collaborative filtering algorithm

as a KTM-based multi-feature fusion exercise recommendation model

SKT-MFER was proposed. Firstly

as a knowledge tracking model

KTM-LC

incorporating student learning behaviors and learning abilities

was constructed to accurately assess the student’s knowledge mastery level. Subsequently

two filters were implemented to ensure the exercise recommendation’s accuracy: the first was an initial screening utilizing the knowledge point mastery matrix to eliminate students who were similar to the target student

and the second was a filtering process considering the combined similarity of cognitive state similarity and exercise difficulty similarity. Through extensive experiments

it proves that the proposed method yields better results than some existing baseline models.

关键词

Keywords

references

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