Machine Learning Techniques for Knowledge Tracing: A Systematic Literature Review
Machine Learning Techniques for Knowledge Tracing: A Systematic Literature Review
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DOI：
10.5220/0010515500600070
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sergio Iván Ramírez Luelmo;N. E. Mawas;J. Heutte
中科院分区：
文献类型：
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作者：
Sergio Iván Ramírez Luelmo;N. E. Mawas;J. Heutte
机器学习（ML）技术正在教育环境中广泛应用。它们被用来预测能力和技能，评分考试，识别行为学术模式，评估开放的答案，建议适当的教育资源，并将具有相似学习特点或学术兴趣的学生分组或联系起来。知识追踪（KT）允许对学习者的技能掌握进行建模，并有意义地预测学生的表现，因为它在学习者模型（LM）中根据学生以前教育实践的观察结果（例如答案，成绩和/或行为）跟踪学生的知识状态。在这项研究中，我们基于PRISMA方法，从来自5个著名学术来源的628篇文章的原始搜索池中，调查了51篇关于KT计算的常用ML技术。我们确定并审查了ML的相关方面的KT LM，有助于绘制一个更准确的全景的主题，因此，有助于减轻困难的选择一个合适的ML技术KT LM。这项工作致力于MOOC设计师/提供商，教学工程师和研究人员，他们需要概述LM中KT的现有ML技术。
Machine Learning (ML) techniques are being intensively applied in educational settings. They are employed to predict competences and skills, grade exams, recognize behavioural academic patterns, evaluate open answers, suggest appropriate educational resources, and group or associate students with similar learning characteristics or academic interests. Knowledge Tracing (KT) allows modelling the learner's mastery of skill and to meaningfully predict student’s performance, as it tracks within the Learner Model (LM) the knowledge state of students based on observed outcomes from their previous educational practices, such as answers, grades and/or behaviours. In this study, we survey commonly used ML techniques for KT figuring in 51 papers on the topic, out of an original search pool of 628 articles from 5 renowned academic sources, encompassing the latest research, based on the PRISMA method. We identify and review relevant aspects of ML for KT in LM that help paint a more accurate panorama on the topic and hence, contribute to alleviate the difficulty of choosing an appropriate ML technique for KT in LM. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who need an overview of existing ML techniques for KT in LM.