个性化学习推荐研究综述

doi:10.3778/j.issn.1673-9418.2105111

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (1): 21-40.DOI: 10.3778/j.issn.1673-9418.2105111

个性化学习推荐研究综述

吴正洋⁺(), 汤庸, 刘海

华南师范大学计算机学院,广州 510631

收稿日期:2021-05-28 修回日期:2021-09-17 出版日期:2022-01-01 发布日期:2021-09-24
通讯作者: + E-mail: wuzhengyang@m.scnu.edu.cn
作者简介:吴正洋（1980—）,男,河北衡水人,博士,高级工程师,硕士生导师,主要研究方向为教育人工智能、教育数据挖掘、协同教育技术等。
汤庸（1964—）,男,湖南张家界人,博士,教授, 博士生导师,主要研究方向为教育大数据、社交网络、推荐系统等。
刘海（1974—）,男,湖南张家界人,博士,副教授,硕士生导师,主要研究方向为教育数据挖掘、知识图谱、自然语言处理等。
基金资助:
国家自然科学基金广东大数据中心重点项目(U1811263)

Survey of Personalized Learning Recommendation

WU Zhengyang⁺(), TANG Yong, LIU Hai

School of Computer Science, South China Normal University, Guangzhou 510631, China

Received:2021-05-28 Revised:2021-09-17 Online:2022-01-01 Published:2021-09-24
About author:WU Zhengyang, born in 1980, Ph.D., associate professor, M.S. supervisor. His research interests include educational artificial intelligence, educational data mining, collaborative educational technology, etc.
TANG Yong, born in 1964, Ph.D., professor, Ph.D. supervisor. His research interests include educational big data, social network, recommender system, etc.
LIU Hai, born in 1974, Ph.D., associate professor, M.S. supervisor. His research interests include educational data mining, knowledge graph, natural language processing, etc.
Supported by:
Key Project of Guangdong Big Data Center of National Natural Science Foundation of China(U1811263)

摘要/Abstract

摘要：

个性化学习推荐是智能学习的一个研究领域,其目标是在学习平台上给特定学习者提供有效学习资源,从而提升学习积极性与学习效果。虽然现有的推荐方法已被广泛用于教学场景,但教学活动自身的科学规律,使个性化学习推荐在个性化参数设置、推荐目标设定、评价标准设计等方面具有一定的特殊性。针对上述问题,在调研大量文献的基础上对近年来个性化学习推荐的研究进行了综述。从学习推荐通用框架、学习者建模、学习推荐对象建模、学习推荐算法、学习推荐评价五方面对个性化学习推荐的相关研究进行了系统的梳理和解读。首先提出了学习推荐系统的通用框架,其次介绍了学习者建模的思路和方法,接着讨论了学习推荐对象建模的思路和方法,然后归纳了学习推荐的算法与模型,接下来总结了学习推荐评价的设计与方法。并对这五方面现有研究的主要思想、实施方案、优势及不足进行了分析。最后还展望了个性化学习推荐未来的发展方向,为智能学习的进一步深入研究奠定了基础。

Abstract:

Personalized learning recommendation is a research field of intelligent learning. Its goal is to provide specific learners with effective learning resources on the learning platform, thereby enhancing learning enthusiasm and learning effect. Although the existing recommendation methods have been widely used in learning scenarios, the scientific rules of learning activities make personalized learning recommendations unique in terms of personalized parameter setting, recommendation goal setting, and evaluation standard design. In response to the above-mentioned problems, the research of personalized learning recommendation in recent years is reviewed on the basis of investigating a large number of literatures. The research on personalized learning recommendation is systematically sorted out and interpreted from five aspects, i.e., the general framework of learning recommendation, learner modeling, learning recommendation object modeling, learning recommendation algorithm, and learning recommendation evaluation. Firstly, the general framework of learning recommendation system is proposed. Secondly, the ideas and methods of learner modeling are introduced. Next, the ideas and methods of learning recommendation object modeling are discussed. Then, this paper summarizes the algorithm and model of learning recommendation. The following, this paper summarizes the design and method of learning recommendation evaluation. This paper also analyzes the main ideas, implementation plans, advantages and disadvantages of the existing research in these five aspects. Finally, this paper also looks forward to the future development direction of personalized learning recommendation, which lays foundation for further in-depth research on intelligent learning.

Key words: learner model, recommendation algorithm, knowledge tracking, graph neural network, heterogeneous information network

中图分类号:

TP391

吴正洋, 汤庸, 刘海. 个性化学习推荐研究综述[J]. 计算机科学与探索, 2022, 16(1): 21-40.

WU Zhengyang, TANG Yong, LIU Hai. Survey of Personalized Learning Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 21-40.

图/表 13

参考文献 112

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文献	主要方法/技术	推荐策略	优势/局限	适用场景
[53]	CNN	CR,基于描述文本生成的学习资源表示结合学习者偏好预测评分进行推荐	文本描述有利于对学习资源建模提供更丰富的特征信息	具有文本描述的学习资料推荐
[62]	Felder-Silverman学习风格量表	CR,基于满足规则的情况计算学习资源与学习者的相关性分数,再根据分数排序推荐	需要人工定义学习资源与学习者学习风格的关联规则	学习资料推荐
[63]	Fuzzy decision tree,CNN	CR,从大量数据中识别出学习资源特征,再根据学习者的理解水平进行推荐	学习资源数据描述模型的性能影响推荐的效果	学习资料推荐
[55]	BPR成对排序	CF,通过捕获学习者对课程两两之间的偏好排序形成课程有序队列	由于负课程均值采样空间较大,这些课程可能不是最优	课程推荐
[60]	Weka API的潜在语义分析	CF,通过关键字注释将学习者与学习资源建立关联	使用关键字注释标签对学习者建模,标签本身源自人工设置,可能导致主观偏差	学习资料推荐
[61]	人工免疫系统（AIS）算法	HR,根据过往课程学习效果对学习者建模,使用AIS聚类融合基于项目的协同过滤获得备选课程的预测矩阵	能够融合多种特征到学习者模型,但对冷启动的缓解效果有限	课程推荐
[67]	动态本体映射	HR,采用相似结构层次的本体映射课程属性与学习者属性,根据匹配度进行推荐	可整合来自多个来源的信息,以提高效率和用户满意度	课程推荐
[68]	遗传算法	HR,整合协同过滤推荐和基于内容推荐,并用遗传算法配置推荐系统的最优参数	可整合学习者与课程的多种标准,并得到最优配置结果	课程推荐
[69]	序列模式挖掘	HR,基于显式特征的学习者模型进行聚类,通过序列模式挖掘完成学习资源的排序及推荐	有利于解决信息过载和缺乏多样性的问题	课程推荐
[42]	深度知识追踪、模拟退火算法	HR,基于知识追踪模型预测学习者答题的准确度生成备选题集,再采用模拟退火算法从候选题集中抽取多样性练习题组成推荐列表	以学习者答题准确度为目标,并考虑所推荐练习题的多样性和新颖度	练习题推荐
[70]	多目标粒子群优化算法	HR,以学习者规划的时间为约束,以同时满足学习者偏好和学习资源难度最适宜为优化目标进行学习资源推荐	考虑在给定时间规划内的多目标推荐问题	学习资料推荐
[39]	自注意力机制	SR,将学习者在当前会话中所查看主题的历史记录作为输入序列,计算候选线程得分进行推荐	能够在没有太多特征属性情况下捕获学习者当前的选择状态	课程讨论线程推荐
[78]	认知诊断模型、PMF	SR,根据答题会话序列形成的认知诊断模型对学习者建模,再采用PMF预测学生的答题情况,最后根据预测结果进行练习题推荐	动态捕获学习者的知识掌握状态	练习题推荐
[38]	深度知识追踪、遗传算法	SR,基于学习者练习答题会话序列预测学习者知识掌握水平,再通过遗传算法设置试卷各项质量指标,生成推荐试卷	同时动态捕获多个学习者的知识掌握状态	试卷推荐
[46]	本体	KR,使用本体分别对学习者、领域知识和学习行为进行建模	未给出具体实现过程	MOOC课程推荐
[71]	本体	KR,基于本体描述所学知识点和学习目标相关性的学习资源推荐方法	本体中领域知识和推荐规则制定不能避免人工偏差	学习资料推荐
[73]	本体推理和神经网络	KR,基于本体模型对学习者和学习资源进行水平层级分类,根据评估反馈向学习者推荐相应水平层级资源	能够根据学习者的水平状态动态调节推荐内容	课程知识概念相应资源的推荐
[48]	本体	KR,通过半自动化方法构建了E-learning行为的标准本体。基于本体规则进行推荐	能够融合多种个性化参数的学习者模型	课程知识概念和主题的推荐
[74]	本体、语义相似度	KR,对学习资源进行分类并基于本体生成语义表示。根据学习资源与学习者目标的语义相似度实现推荐	无法确保所推荐知识概念的先后顺序相关性	学习中的相关词汇推荐
[75]	知识图谱	KR,基于学习过程中出现的知识单元相关要素构建知识图谱,从而形成多个学习路径,然后根据学习者的学习日志判断其学习进度,再向其推荐学习路径	学习路径的配置既符合规律又具有灵活性	学习路径推荐
[15]	知识图谱	KR,构建了一个以学习目标为导向的跨学习领域知识图谱,其中包括了六种语义关系,然后结合学习者的学习目标和学习资源的特征表示推荐学习路径	可扩展、可重用	跨领域学习路径推荐

文献	主要方法/技术	推荐策略	优势/局限	适用场景
[53]	CNN	CR,基于描述文本生成的学习资源表示结合学习者偏好预测评分进行推荐	文本描述有利于对学习资源建模提供更丰富的特征信息	具有文本描述的学习资料推荐
[62]	Felder-Silverman学习风格量表	CR,基于满足规则的情况计算学习资源与学习者的相关性分数,再根据分数排序推荐	需要人工定义学习资源与学习者学习风格的关联规则	学习资料推荐
[63]	Fuzzy decision tree,CNN	CR,从大量数据中识别出学习资源特征,再根据学习者的理解水平进行推荐	学习资源数据描述模型的性能影响推荐的效果	学习资料推荐
[55]	BPR成对排序	CF,通过捕获学习者对课程两两之间的偏好排序形成课程有序队列	由于负课程均值采样空间较大,这些课程可能不是最优	课程推荐
[60]	Weka API的潜在语义分析	CF,通过关键字注释将学习者与学习资源建立关联	使用关键字注释标签对学习者建模,标签本身源自人工设置,可能导致主观偏差	学习资料推荐
[61]	人工免疫系统（AIS）算法	HR,根据过往课程学习效果对学习者建模,使用AIS聚类融合基于项目的协同过滤获得备选课程的预测矩阵	能够融合多种特征到学习者模型,但对冷启动的缓解效果有限	课程推荐
[67]	动态本体映射	HR,采用相似结构层次的本体映射课程属性与学习者属性,根据匹配度进行推荐	可整合来自多个来源的信息,以提高效率和用户满意度	课程推荐
[68]	遗传算法	HR,整合协同过滤推荐和基于内容推荐,并用遗传算法配置推荐系统的最优参数	可整合学习者与课程的多种标准,并得到最优配置结果	课程推荐
[69]	序列模式挖掘	HR,基于显式特征的学习者模型进行聚类,通过序列模式挖掘完成学习资源的排序及推荐	有利于解决信息过载和缺乏多样性的问题	课程推荐
[42]	深度知识追踪、模拟退火算法	HR,基于知识追踪模型预测学习者答题的准确度生成备选题集,再采用模拟退火算法从候选题集中抽取多样性练习题组成推荐列表	以学习者答题准确度为目标,并考虑所推荐练习题的多样性和新颖度	练习题推荐
[70]	多目标粒子群优化算法	HR,以学习者规划的时间为约束,以同时满足学习者偏好和学习资源难度最适宜为优化目标进行学习资源推荐	考虑在给定时间规划内的多目标推荐问题	学习资料推荐
[39]	自注意力机制	SR,将学习者在当前会话中所查看主题的历史记录作为输入序列,计算候选线程得分进行推荐	能够在没有太多特征属性情况下捕获学习者当前的选择状态	课程讨论线程推荐
[78]	认知诊断模型、PMF	SR,根据答题会话序列形成的认知诊断模型对学习者建模,再采用PMF预测学生的答题情况,最后根据预测结果进行练习题推荐	动态捕获学习者的知识掌握状态	练习题推荐
[38]	深度知识追踪、遗传算法	SR,基于学习者练习答题会话序列预测学习者知识掌握水平,再通过遗传算法设置试卷各项质量指标,生成推荐试卷	同时动态捕获多个学习者的知识掌握状态	试卷推荐
[46]	本体	KR,使用本体分别对学习者、领域知识和学习行为进行建模	未给出具体实现过程	MOOC课程推荐
[71]	本体	KR,基于本体描述所学知识点和学习目标相关性的学习资源推荐方法	本体中领域知识和推荐规则制定不能避免人工偏差	学习资料推荐
[73]	本体推理和神经网络	KR,基于本体模型对学习者和学习资源进行水平层级分类,根据评估反馈向学习者推荐相应水平层级资源	能够根据学习者的水平状态动态调节推荐内容	课程知识概念相应资源的推荐
[48]	本体	KR,通过半自动化方法构建了E-learning行为的标准本体。基于本体规则进行推荐	能够融合多种个性化参数的学习者模型	课程知识概念和主题的推荐
[74]	本体、语义相似度	KR,对学习资源进行分类并基于本体生成语义表示。根据学习资源与学习者目标的语义相似度实现推荐	无法确保所推荐知识概念的先后顺序相关性	学习中的相关词汇推荐
[75]	知识图谱	KR,基于学习过程中出现的知识单元相关要素构建知识图谱,从而形成多个学习路径,然后根据学习者的学习日志判断其学习进度,再向其推荐学习路径	学习路径的配置既符合规律又具有灵活性	学习路径推荐
[15]	知识图谱	KR,构建了一个以学习目标为导向的跨学习领域知识图谱,其中包括了六种语义关系,然后结合学习者的学习目标和学习资源的特征表示推荐学习路径	可扩展、可重用	跨领域学习路径推荐

个性化学习推荐研究综述

Survey of Personalized Learning Recommendation

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