Learning Analytics Intervention: A Review of Case Studies
Learning Analytics Intervention: A Review of Case Studies
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DOI：
10.1109/iset.2018.00047
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
B. Wong;K. Li
中科院分区：
文献类型：
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作者：
B. Wong;K. Li
干预被认为是学习分析中最大的挑战。作为为学习者提供及时和个性化的支持，干预尚未在学习分析实践中广泛实施。本文回顾了23个高等教育案例的干预实践。个案按干预方法的性质分为四类：直接讯息、可付诸行动的反馈、学生分类及重新设计课程；并对干预方法进行了总结。干预病例多属于前两类。直接信息包括通过电子邮件或电话等渠道联系有风险的学生，鼓励他们参与，提供额外的学习资源，或提醒他们截止日期。可操作的反馈包括为学生提供建议或信息，帮助他们了解自己的表现和可能的改进方法。其他两种类型的干预仅确定了少数案例，这两种干预涉及根据学生的风险水平将他们分为不同的组，以便为每个组采取具体的补救措施，并根据数据分析重新设计课程结构或内容。这项检讨的结果，有助于高等教育机构制定干预策略，实践学习分析。
Intervention has been claimed to be the greatest challenge in learning analytics. As the provision of just-in-time and personalised support for learners, intervention has yet to be widely implemented in learning analytics practices. This paper reviews intervention practices in higher education in 23 case studies. The cases were categorised into four types – direct message, actionable feedback, categorisation of students, and course redesign – according to the nature of the methods of intervention; and the intervention methods were summarised. Most of the intervention cases belonged to the first two types. Direct message involves contacting at-risk students via channels such as emails or phone calls to encourage their participation, provide additional learning resources, or remind them of deadlines. Actionable feedback involves the provision of suggestions or information for students to help them understand their performance and possible ways of improving it. Only a few cases were identified for the other two types of intervention, which involve categorisation of students into different groups based on their risk levels for taking specific remedial actions for each group, and redesigning the course structure or contents based on the analysis of data. The results of this review serve to facilitate the formulation of intervention strategies for higher education institutions which practise learning analytics.