Students' performance prediction model using meta-classifier approach

Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive...

Full description

Saved in:
Bibliographic Details
Main Authors: Hassan, H., Anuar, S., Ahmad, N. B.
Format: Conference or Workshop Item
Published: 2019
Subjects:
Online Access:http://eprints.utm.my/id/eprint/91213/
http://www.dx.doi.org/10.1007/978-3-030-20257-6_19
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
id my.utm.91213
record_format eprints
spelling my.utm.912132021-06-21T08:41:06Z http://eprints.utm.my/id/eprint/91213/ Students' performance prediction model using meta-classifier approach Hassan, H. Anuar, S. Ahmad, N. B. QA75 Electronic computers. Computer science Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students’ dropout rate. Hence, the purpose of this research is to identify factors influencing students’ performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students’ information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers; Decision Tree, Artificial Neural Network and Support Vector Machine, and 5 multi-classifiers; Random Forest, Bagging, AdaBoost, Stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new Students’ Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students’ performance. 2019 Conference or Workshop Item PeerReviewed Hassan, H. and Anuar, S. and Ahmad, N. B. (2019) Students' performance prediction model using meta-classifier approach. In: 20th International Conference on Engineering Applications of Neural Networks, EANN 2019, 24-26 May 2019, Xersonisos, Greece. http://www.dx.doi.org/10.1007/978-3-030-20257-6_19
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hassan, H.
Anuar, S.
Ahmad, N. B.
Students' performance prediction model using meta-classifier approach
description Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students’ dropout rate. Hence, the purpose of this research is to identify factors influencing students’ performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students’ information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers; Decision Tree, Artificial Neural Network and Support Vector Machine, and 5 multi-classifiers; Random Forest, Bagging, AdaBoost, Stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new Students’ Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students’ performance.
format Conference or Workshop Item
author Hassan, H.
Anuar, S.
Ahmad, N. B.
author_facet Hassan, H.
Anuar, S.
Ahmad, N. B.
author_sort Hassan, H.
title Students' performance prediction model using meta-classifier approach
title_short Students' performance prediction model using meta-classifier approach
title_full Students' performance prediction model using meta-classifier approach
title_fullStr Students' performance prediction model using meta-classifier approach
title_full_unstemmed Students' performance prediction model using meta-classifier approach
title_sort students' performance prediction model using meta-classifier approach
publishDate 2019
url http://eprints.utm.my/id/eprint/91213/
http://www.dx.doi.org/10.1007/978-3-030-20257-6_19
_version_ 1703960437307998208