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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Students Performance Analysis Based on Machine Learning Techniques
verfasst von : Alberto Rivas, Jesús M. Fraile, Pablo Chamoso, Alfonso González-Briones, Sara Rodríguez, Juan M. Corchado
Erschienen in: Learning Technology for Education Challenges
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Abstract
One of the main concerns of educational entities is improving the quality of teaching and the academic performance of students. As a result of this concern, countless studies have been performed to identify the factors that affect student’s learning. These have helped to guide students in the correct direction and to change for the better their students’ habits and personal situation. Since the appearance of virtual classrooms, the monitoring of the students’ use of online resources has allowed teachers to identify their learning habits, analysing some of the reasons for their academic progress or the lack thereof.
This research analyses different machine learning techniques, including tree based models and different types of Neural Networks. The objective is to apply those models to a dataset containing data from a virtual environment and construct performance models that will allow to predict if a student is going to fail or pass the academic year. Finally, the factors that have a greatest influence on the performance of a student are identified and suggestions for the improvement of those factors are proposed in order to achieve an increase the pass rate among students.