Metamodeling for Learning Analytics in Higher Education
DOI:
https://doi.org/10.46328/ijte.7748Keywords:
adaptive education, learning analytics, metamodeling, optimization, weighted attributes, educational data miningAbstract
In modern higher education, extensive learning log data is generated, capturing students' behavioral, temporal, and cognitive activity in online courses. Transforming these diverse data streams into understandable, interpretable, and actionable insights for adaptive learning is an important research challenge. This study presents a metamodeling approach to learning analytics that applies principles of educational analytics engineering. The data model was created using learning logs from six CAD e-courses, which involved 155 students and utilized various teaching methods. Each online course was weighted based on ECTS credits and the number of students, ensuring hierarchical normalization and data comparability across courses. Six machine learning models were trained to predict dropout risk. Three models - Decision Tree, Random Forest, and Gradient Boosting - showed similarly high validation accuracy, but the Gradient Boosting model was selected for further analysis due to its stability and interpretability within the metamodeling framework. The results confirmed that metamodeling improves the interpretation and use of data to understand learning dynamics. By considering educational processes as design systems, this study proposes an approach to using surrogate models in adaptive learning environments. The proposed metamodel supports the development of teacher-centered early warning systems in learning management systems such as Moodle.
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