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Student profiling in a dispositional learning analytics application using formative assessment
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DOI：10.1016/j.chb.2017.08.010.png)
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Abstract
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How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It extends previous work (Tempelaar, Rienties, & Giesbers, 2015), where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Learning analytics
Formative assessment
Learning dispositions
Dispositional learning analytics
e-tutorial
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