Refining the Learning Analytics Capability Model
| Authors | Justian Knobbout, Esther van der Stappen, Johan Versendaal |
|---|---|
| Published in | AMCIS 2020 Proceedings 9 |
| Publication date | 10 augustus 2020 |
| Research groups | Betekenisvol Digitaal Innoveren |
| Type | Lecture |
Summary
Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome.
On this publication contributed
| Language | Engels |
|---|---|
| Published in | AMCIS 2020 Proceedings 9 |
| Key words | learning analytics , higher education , Learning Analytics Capability Model |
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