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Improving learning analytics – Combining observational and self-report data on student learning
Ellis, Robert A. Han, Feifei Pardo, Abelardo
Ellis, Robert A.
Han, Feifei
Pardo, Abelardo
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Abstract
The field of education technology is embracing a use of learning analytics to improve student experiences of learning. Along with exponential growth in this area is an increasing concern of the interpretability of the analytics from the student experience and what they can tell us about learning. This study offers a way to address some of the concerns of collecting and interpreting learning analytics to improve student learning by combining observational and self-report data. The results present two models for predicting student academic performance which suggest that a combination of both observational and self-report data explains a significantly higher variation in student outcomes. The results offer a way into discussing the quality of interpretations of learning analytics and their usefulness for helping to improve the student experience of learning and also suggest a pathway for future research into this area.
Keywords
learning analytics, self-report data, observation, student approaches to learning
Date
2017
Type
Journal article
Journal
Educational Technology and Society
Book
Volume
20
Issue
3
Page Range
158-169
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
DOI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY-NC-ND 3.0
File Access
Open