RT Journal Article
T1 Artificial neural network model to predict student performance using nonpersonal information
A1 Chavez, Heyul
A1 Chávez Arias, Bill
A1 Contreras Rosas, Sebastián
A1 Álvarez Rodríguez, José María
A1 Raymundo, Carlos
AB 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.
PB Frontiers Media
SN 2504-284X
YR 2023
FD 2023-02-09
LK https://hdl.handle.net/10016/36691
UL https://hdl.handle.net/10016/36691
LA eng
DS e-Archivo
RD 30 jun. 2024