Using artificial intelligence to predict academic performance

Quang Hung Do

ISBN 10: 3659779792 / ISBN 13: 9783659779794

Published by LAP LAMBERT Academic Publishing Sep 2015, 2015

About this Item

Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 92 pp. Englisch.

Seller Inventory # 9783659779794

Bibliographic Details

• Title: Using artificial intelligence to predict academic performance
• Publisher: LAP LAMBERT Academic Publishing Sep 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3659779792
• ISBN 13: 9783659779794
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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.