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One important aspect of motivation is engagement. In order to learn, students need to be engaged in the learning activities. However, that does not always happen due to various factors. This paper investigates the possibility to detect the level of engagement of a learner using an e-learning system. More specifically, we are looking for actions that could predict it. Using log files analysis we found that these actions are related to reading pages and taking tests, which are common to every e-Learning system. Several experiments showed that predictions based on attributes related to these two actions are as good as those that include a larger number of actions available in an e-Learning system. A comparison between the attributes found relevant in our research and the attributes used in previous research shows the consistency of our findings. The novelty of our approach is that the focus is on the learning time rather that on evaluation through quizzes-type activities.
|Title of host publication||Artificial intelligence in education: building technology rich learning contexts that work|
|Editors||R. Luckin, K. Koedinger, J. Greer|
|Place of Publication||Washington|
|Number of pages||2|
|Publication status||Published - 2007|
|Event||AIED - |
Duration: 1 Jan 2007 → …
|Name||Frontiers in artificial intelligence and applications|
|Period||1/01/07 → …|
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- 1 Finished
Haig, E. & Weibelzahl, S.
2/01/06 → 30/09/07