Using artificial intelligence to predict academic performance

Quang Hung Do

ISBN 10: 3659779792 / ISBN 13: 9783659779794

Verlag: LAP LAMBERT Academic Publishing Sep 2015, 2015

Beschreibung

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.

Bestandsnummer des Verkäufers 9783659779794

Bibliografische Details

• Titel: Using artificial intelligence to predict academic performance
• Verlag: LAP LAMBERT Academic Publishing Sep 2015
• Erscheinungsjahr: 2015
• Sprache: Englisch
• ISBN-10: 3659779792
• ISBN-13: 9783659779794
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.