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Student profiling in a dispositional learning analytics application using formative assessment

Tempelaar, Dirk; Rienties, Bart; Mittelmeier, Jenna and Nguyen, Quan (2017). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78 pp. 408–420.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.chb.2017.08.010
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Abstract

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 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.

Item Type: Journal Item
Copyright Holders: 2017 Elsevier Ltd.
ISSN: 0747-5632
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Research Group: OpenSpace Research Centre (OSRC)
Item ID: 50427
Depositing User: Bart Rienties
Date Deposited: 08 Aug 2017 15:08
Last Modified: 20 Nov 2018 01:50
URI: http://oro.open.ac.uk/id/eprint/50427
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