The Predictive Model of Higher Education Guidance for Information Overload of Learner Groups Using Hybrid Ensemble Techniques

• Published in: TEM Journal Vol. 11; no. 4; pp. 1792 - 1803
• Main Authors: Surawatchayotin, Atsawin, Paireekreng, Worapat, Imsombut, Aurawan
• Format: Journal Article
• Language: English
• Published: Novi Pazar UIKTEN - Association for Information Communication Technology Education and Science 01.11.2022
• Subjects: Accuracy; Algorithms; Classification; Cognitive style; Colleges & universities; Data mining; Decision making; Deep learning; Education; Ensemble learning; Higher education; ICT Information and Communications Technologies; Information overload; Labor market; Machine learning; Neural networks; Prediction models; Skills; Students; Ensemble deep learning; neural networks autoencoders; information overload
• ISSN: 2217-8309; 2217-8333; 2217-8333
• DOI: 10.18421/TEM114-47

The decision-making for a suitable area of study in the university seems to be a crucial task for students. The machine learning technique can help provide alternatives based on user profiles. This research proposes an improved predictive model of the subject area for learner groups in higher education. The proposed techniques are focused on hybrid ensemble learning techniques to optimize traditional predictor-building practices by Dimensionality Reduction to model by Neural Networks Autoencoders (NNAE). The results showed that the proposed ensemble NNAE techniques performed better than other ensemble techniques.