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A Time Series Interaction Analysis Method for Building Predictive Models of Learners Using Log Data

Item

Title
A Time Series Interaction Analysis Method for Building Predictive Models of Learners Using Log Data
Abstract/Description
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.
Author/creator
Brooks, Christopher
Thompson, Craig
Teasley, Stephanie
Date
2015
In publication
Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
Series
LAK '15
Pages
126–135
Publisher
Association for Computing Machinery
Resource type
en Research/Scholarly Media
Resource status/form
en Published Text
Scholarship genre
en Empirical
Open access/full-text available
en No
Peer reviewed
en No
ISBN
978-1-4503-3417-4
DOI
10.1145/2723576.2723581
URL
Official Publisher's Webpage (ACM Digital Library)
Citation
Brooks, C., Thompson, C., & Teasley, S. (2015). A Time Series Interaction Analysis Method for Building Predictive Models of Learners Using Log Data. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 126–135. https://doi.org/10.1145/2723576.2723581
Place
New York, NY, USA
Item sets
Handbook Chapter 20 Citations

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