Adaptive e-learning systems (AeLS), which emerged in the late 1990s, offer an alternative to the 'one-size-fits-all' approach by addressing the demand for individualized learning experiences. These systems typically consist of five elements, including a domain model, a media space, an adaptation model, a user interface, and a learner model. Despite the increasing academic interest in this topic and the rapid development of techniques for adaptation over the past decade, there remains a significant gap in reviews that investigate learner characteristics and the techniques used for characteristic identification. To bridge this gap, we conducted a systematic review with a total of 57 studies reported from 2013 to 2023 to provide a comprehensive overview of the current trends in adaptive e-learning system research. While this review may serve as a reference for setting up a learner model as it provides the landscape of techniques utilized in recent studies, our review revealed a scarcity of research on the development of the learner model, particularly the studies that share clear theoretical or empirical justification of the techniques used for adaptation. We recommend incorporating multiple relevant learner characteristics in learner model and providing clear rationales for selecting these characteristics. We also suggest that future research should consider incorporating adaptive assessment more extensively in AeLSs.

中文翻译：

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自适应电子学习系统中学习者模型的发展和技术：系统评价


1990 年代后期出现的自适应电子学习系统 （AeLS） 通过满足对个性化学习体验的需求，为“一刀切”方法提供了一种替代方案。这些系统通常由五个元素组成，包括域模型、媒体空间、适应模型、用户界面和学习者模型。尽管学术界对这一主题的兴趣日益浓厚，并且在过去十年中适应技术发展迅速，但在调查学习者特征和用于特征识别技术的综述方面仍然存在很大差距。为了弥合这一差距，我们进行了一项系统评价，从 2013 年到 2023 年共报告了 57 项研究，以全面概述自适应电子学习系统研究的当前趋势。虽然这篇综述可以作为建立学习者模型的参考，因为它提供了最近研究中使用的技术的前景，但我们的综述揭示了关于学习者模型发展的研究稀缺，特别是那些对用于适应的技术有明确理论或实证证明的研究。我们建议在学习者模型中合并多个相关的学习者特征，并提供选择这些特征的明确理由。我们还建议未来的研究应考虑更广泛地将适应性评估纳入 AeLSs。