Abstract
This paper presents results from the continuous process of developing an evaluation framework of quality indicators for learning analytics (LA). Building on a previous study, a group concept mapping approach that uses multidimensional scaling and hierarchical clustering, the study presented here applies the framework to a collection of LA tools in order to evaluate the framework. Using the quantitative and qualitative results of this study, the rst version of the framework was revisited so as to allow work towards an improved version of the evaluation framework of quality indicators for LA.
| Original language | English |
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| Title of host publication | Proceedings of the Fifth International Conference on Learning Analytics And Knowledge |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 16-20 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4503-3417-4 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | The 5th International Learning Analytics and Knowledge (LAK) Conference: Scaling Up: Big Data to Big Impact - Marist College, Poughkeepsie, United States Duration: 16 Mar 2015 → 20 Mar 2015 Conference number: 5 http://lak15.solaresearch.org/home |
Conference
| Conference | The 5th International Learning Analytics and Knowledge (LAK) Conference |
|---|---|
| Abbreviated title | LAK'15 |
| Country/Territory | United States |
| City | Poughkeepsie |
| Period | 16/03/15 → 20/03/15 |
| Internet address |
Keywords
- evaluation framework
- learning analytics
- quality indicators
- group concept mapping
- assessment of learning analytics tools