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 related 123 218 C. Vialardi et al.courses. 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.
Currently many methods and tools are being developed to support e-Learning courses. On the one hand, they are used to help students. On the other, a few applications are being developed to help course designers and instructors. In addition, the development of this applications is important for improving the performance of the course. Thus, we proposed in this paper to use data mining methods to aid in the designing of adaptive courses and the evaluation of their effectiveness. Lastly, the results of the implementation of our tool and examples of the utility of Data Mining for teachers is given.
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