Publication: Artificial neural network model to predict student performance using nonpersonal information
Loading...
Identifiers
Publication date
2023-02-09
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers Media
Impact
Export
Abstract
In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students¿ academic performance based on personal information such as social status, income, address, etc. This study proposes and develops an artificial neural network model capable of determining whether a student will pass a certain class without using personal or sensitive information that may compromise student privacy. For model training, we used information regarding 32,000 students collected from The Open University of the United Kingdom, such as number of times they took the course, average number of evaluations, course pass rate, average use of virtual materials per date and number of clicks in virtual classrooms. Attributes selected for the model are as follows: 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-score. These results will help the student authorities to take measures to avoid withdrawal and underachievement.
Description
Keywords
privacy, personal data, neural networks, forecasting, academic performance
Bibliographic citation
Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Álvarez-Rodríguez, J.M., Raymundo, C. (2023). Artificial neural network model to predict student performance using nonpersonal information. Frontiers in Education, 8, 1106679