Tempelaar, D. T.; Rienties, B. and Giesbers, B.
(2015).
|
PDF (Accepted Manuscript)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (544kB) | Preview |
| Google Scholar: | Look up in Google Scholar |
|---|
Abstract
Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation.
| Item Type: | Conference or Workshop Item |
|---|---|
| ISBN: | 989-758-107-3, 978-989-758-107-6 |
| Extra Information: | Winner of Best Paper Award 2015 |
| Academic Unit/School: | Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET) Learning and Teaching Innovation (LTI) |
| Item ID: | 43446 |
| Depositing User: | Bart Rienties |
| Date Deposited: | 12 Jun 2015 09:41 |
| Last Modified: | 02 May 2018 14:09 |
| URI: | http://oro.open.ac.uk/id/eprint/43446 |
| Share this page: |     |
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)