A multi-feature fusion exercise recommendation model based on knowledge tracing machines

ZHUGE Bin; WANG Ying; XIAO Mengfan; YAN Lei; WANG Bingyan; DONG Ligang; JIANG Xian

doi:10.11959/j.issn.1000-0801.2024210

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A multi-feature fusion exercise recommendation model based on knowledge tracing machines

Research and Development | 更新时间：2024-11-25

- A multi-feature fusion exercise recommendation model based on knowledge tracing machines
- Telecommunications Science Vol. 40, Issue 9, Pages: 75-87(2024)
- 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：
- 基金信息：
  
  Zhejiang Province “Spearhead” and “Leading Wild Goose” Research and Development Project(2023C03202);Key Project of Higher Education Research of Zhejiang Higher Education Association in 2022(KT2022017);2023 General Research Project of Zhejiang Provincial Department of Education(Y202353291);The“14th Five-Year Plan”Teaching Reform Project of General Undergraduate Universities in Zhejiang Province(jg20220247)
- DOI：10.11959/j.issn.1000-0801.2024210
  CLC： TP301
- Received：18 June 2024，
  
  Revised：2024-08-16，
  
  Published：20 September 2024
- 稿件说明：
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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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