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Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study

1
Universidad de Las Américas, Facultad de Ingeniería y Ciencias Aplicadas, Av. de los Granados E12-41 y Colimes, Quito EC170125, Ecuador
2
Universidad de Alicante, Departamento de Tecnología Informática y Computación, San Vicente del Raspeig, 03690 Alicante, Spain
3
Universidad de Alicante, Departamento de Lenguajes y Sistemas Informáticos, San Vicente del Raspeig, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(10), 2833; https://doi.org/10.3390/su11102833
Received: 7 April 2019 / Revised: 2 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
PDF [2929 KB, uploaded 17 May 2019]

Abstract

The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students.
Keywords: educational data mining; learning analytics; machine learning; big data; prediction grades educational data mining; learning analytics; machine learning; big data; prediction grades
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Buenaño-Fernández, D.; Gil, D.; Luján-Mora, S. Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study. Sustainability 2019, 11, 2833.

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