Application of artificial intelligence to predict academic performance in order to prevent dropout in high school students: Systematic Literature Review

Salazar Alberto R.A.; Guzman Aquije E.H.; Lozada Flores R.M. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/laccei2024.1.1.1426

Resumen

This paper presents a systematic literature review on the application of artificial intelligence to predict student dropout in higher education. It highlights that student dropout not only affects individuals, but also institutional resources and society in general. To address this problem, the application of artificial intelligence was proposed as a tool to anticipate and enhance the academic performance of students. Through the identification of risk factors related to dropout and the provision of personalized intervention strategies. In doing so, the aim is to improve student retention and academic success. In the systematic literature review presented here, the PICO and PRISMA methodologies were used. For this purpose, Scopus databases were exhaustively searched and a total of 110 records were identified. After applying the inclusion and exclusion criteria, 18 academic articles were selected for review. The results of the systematic review indicate that the application of artificial intelligence can be effective in predicting student dropout and improving student retention. Different machine learning and deep learning models were found to be used to identify at-risk students and provide personalized recommendations. In addition, the importance of collecting and analyzing historical data to improve the accuracy of forecasting models was emphasized.

Instituciones: Universidad Tecnológica del Perú