Resumen
Student academic performance at universities is crucial for education
management systems. Many actions and decisions are made based on it, specifically the enrollment process. During enrollment, students have to decide which courses to sign up for. This research presents the rationale behind the design of a recommender system to support the enrollment process using the students’ academic performance
record. To build this system, the CRISP-DM methodology was applied to data from students of the Computer Science Department at University of Lima, Perú. One of the main contributions of this work is the use of two synthetic attributes to improve the relevance of the recommendations made. The first attribute estimates the inherent
difficulty of a given course. The second attribute, named potential, is a measure of the competence of a student for a given course based on the grades obtained in relatedcourses. Data was mined using C4.5, KNN (K-nearest neighbor), Naïve Bayes, Bagging and Boosting, and a set of experiments was developed in order to determine the best algorithm for this application domain. Results indicate that Bagging is the best
method regarding predictive accuracy. Based on these results, the “Student Performance Recommender System” (SPRS) was developed, including a learning engine. SPRS was tested with a sample group of 39 students during the enrollment process. Results showed that the system had a very good performance under real-life conditions.
Idioma original | Inglés estadounidense |
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Publicación | User Modeling and User-Adapted Interaction |
DOI | |
Estado | Publicada - 1 ene. 2011 |
Publicado de forma externa | Sí |
COAR
- Artículo
Categorías Repositorio Ulima
- Ciencias sociales / Educación
Temas Repositorio Ulima
- Administración de sistemas de información
- Data mining