Summary of Improving the Portability Of Predicting Students Performance Models by Using Ontologies, By Javier Lopez Zambrano et al.
Improving the portability of predicting students performance models by using ontologies
by Javier Lopez Zambrano, Juan A. Lara, Cristobal Romero
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach aims to improve the portability of predictive models in Educational Data Mining and Learning Analytics by utilizing high-level attributes with semantic meaning, such as an ontology that summarizes students’ interactions with a Moodle learning management system. By comparing the results of this approach with those obtained using low-level raw attributes, the study demonstrates that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive models can be used to personalize education and improve student outcomes. However, these models are often trained on specific data sets and may not generalize well to other courses or contexts. This paper proposes a solution by using ontological models that summarize students’ interactions with learning management systems like Moodle. The results show that these models can be applied to different target courses without losing predictive accuracy. |
