Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Rahimah A. Halim, R. Mohemad, N. Ali
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Abstract

Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.
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适应性学习系统中学生特征的识别:系统文献综述
适应性学习允许学生根据自己的能力和特点,按照自己的节奏进行有效的学习。目前,关于学生适应性学习特征的单反很多,前人从不同的角度对其进行了分析。然而,必须指出的是,这些研究的结果是基于2010年至2020年之间进行的分析。该SLR旨在扩展和弥合在识别实施自适应学习系统的基本学生特征方面的差距。它特别关注最近五年,从2018年到2022年。本研究按照规定的纳入和排除标准选择了39篇文献。SLR的研究结果表明,学习风格是研究文章中最常用的适应因素,其次是知识特征、认知特征、学生偏好和动机。该研究还发现,大多数被审查的文章在建模学生模型时使用了多个学生的特征,结果表明教育工作者在教学和学习过程中整合了在线学习以实施适应性学习。传统的检测工具ILS被广泛用于学习风格数据的收集,除了FSLSM之外,还使用VARK、Kolb、Honey & Mumford等其他学习风格模型来分配学生的学习风格偏好。
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