基于学习分析的干预:实验研究的系统回顾

Mustafa Tepgeç, Dirk Ifenthaler
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引用次数: 2

摘要

学习分析包括支持学习和改善学习环境的干预措施。尽管学习分析是一个很有前途的研究领域,但近年来,关于基于学习分析的干预措施的影响缺乏经验证据的问题已经得到了广泛的解决。在这种情况下,通过实验研究验证的见解可能起着至关重要的作用。因此,有必要撰写一份报告,描述当前基于学习分析的实验干预的方法学方面和效果。本系统综述对学习分析研究进行了深入的研究,报告了实验结果,以评估基于学习分析的干预措施。PRISMA(系统评价和荟萃分析首选报告项目)2020方案为本系统评价的工作提供了基础。本综述包含52篇符合纳入和排除标准的论文。结果表明,面向学生的仪表板是最常见的基于学习分析的干预措施。如何处理用户数据以进行干预的证据表明,最常见的方法是对数据进行提炼,以供人类判断。本研究证实,很大一部分采用学习分析干预的实验研究已经证明对学习结果有显著影响。基于学习分析的干预的有效性也在这篇综述中讨论了动机、参与和系统使用行为。本研究的结果将有助于深入描述基于学习分析的干预的实验验证结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LEARNING ANALYTICS BASED INTERVENTIONS: A SYSTEMATIC REVIEW OF EXPERIMENTAL STUDIES
Learning analytics includes interventions that will support learning and improve learning environments. Despite the fact that learning analytics is a promising field of study, the lack of empirical evidence on the effects of learning analytics-based interventions has been widely addressed in recent years. In this context, insights validated by experimental studies may play a crucial role. Therefore, there is a need for a report describing the methodological aspects and effects of current experimental interventions based on learning analytics. This systematic review provides an in-depth examination of learning analytics research that reports experimental findings to evaluate learning analytics-based interventions. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 protocol provided the basis for the work of this systematic review. This review contained 52 papers that met the inclusion and exclusion criteria. The results show that student-facing dashboards are the most common learning analytics-based intervention. Evidence from how user data is handled for interventions demonstrates that the most common method is the distillation of data for human judgment. This study confirms that a significant proportion of experimental studies employing learning analytics interventions have demonstrated significant effects on learning outcomes. The effectiveness of learning analytics-based interventions is also addressed in this review in terms of motivation, engagement, and system usage behaviors. The findings of this study will contribute to the literature in terms of describing the experimentally validated findings of learning analytics-based interventions in depth.
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