Improving learning analytics – Combining observational and self-report data on student learning
Journal article
Ellis, Robert A., Han, Feifei and Pardo, Abelardo. (2017). Improving learning analytics – Combining observational and self-report data on student learning. Educational Technology and Society. 20(3), pp. 158-169.
| Authors | Ellis, Robert A., Han, Feifei and Pardo, Abelardo |
|---|---|
| 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 |
| Year | 2017 |
| Journal | Educational Technology and Society |
| Journal citation | 20 (3), pp. 158-169 |
| Publisher | International Forum of Educational Technology and Society |
| ISSN | 1176-3647 |
| Scopus EID | 2-s2.0-85021708164 |
| Web address (URL) | https://www.jstor.org/stable/26196127 |
| Open access | Published as ‘gold’ (paid) open access |
| Page range | 158-169 |
| Funder | Australian Research Council (ARC) |
| Publisher's version | License File Access Level Open |
| Output status | Published |
| Publication dates | |
| Online | Jul 2017 |
| Publication process dates | |
| Accepted | 13 Sep 2016 |
| Deposited | 05 Jul 2023 |
| ARC Funded Research | This output has been funded, wholly or partially, under the Australian Research Council Act 2001 |
| Grant ID | DP150104163 |
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| License: CC BY-NC-ND 3.0 | |
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