Using artificial intelligence to predict academic performance

This book develops a validated set of models to predict student academic performance at university. Different models are developed by using artificial intelligence techniques (i.e., Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Cuckoo Search algorithm) and a combination of predictor variables. The predictor variables include the results of standardized exams and other factors, such as socio-economic background and student study habits, that may likely influence student performance. The results of the prediction ability of each model are investigated, analyzed, and discussed. The developed models can work as an advisory reference for lecturers in preparing course contents and learning materials. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Quang Hung Do, PhD, is currently with University of Transport Technology (Vietnam). He was a teaching assistant and a researcher at Feng Chia University (Taiwan). He served as a reviewer of several ISI-indexed journals, such as, Applied Soft Computing, Iranian Journal of Fuzzy Systems. His current research interests include fuzzy and AI techniques.