A time series interaction analysis method for building predictive models of learners using log data
A time series interaction analysis method for building predictive models of learners using log data
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DOI：
10.1145/2723576.2723581
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Christopher A. Brooks;Craig D. S. Thompson;Stephanie D. Teasley
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文献类型：
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作者：
Christopher A. Brooks;Craig D. S. Thompson;Stephanie D. Teasley
随着课程规模变得越来越大、转向在线并以低成本向公众部署（例如通过大规模开放在线课程，MOOC），需要新的预测学生成绩的方法来支持学习过程。本文提出了一种将教育日志数据转换为适合构建学生成功预测模型的特征的新颖方法。与认知建模或内容分析方法不同，这些模型是根据学习者和资源之间的交互构建的，这种方法不需要教学或领域专家的输入，并且可以跨课程或学习环境应用。
As courses become bigger, move online, and are deployed to the general public at low cost (e.g. through Massive Open Online Courses, MOOCs), new methods of predicting student achievement are needed to support the learning process. This paper presents a novel method for converting educational log data into features suitable for building predictive models of student success. Unlike cognitive modelling or content analysis approaches, these models are built from interactions between learners and resources, an approach that requires no input from instructional or domain experts and can be applied across courses or learning environments.