Vol.11, No.4, November 2022.                                                                                                                                                                              ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

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


Atsawin Surawatchayotin, Worapat Paireekreng, Aurawan Imsombut


© 2022 Atsawin Surawatchayotin, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 11, Issue 4, Pages 1792-1803, ISSN 2217-8309, DOI: 10.18421/TEM114-47, November 2022.


Received: 27 August 2022.

Revised:   03 Octobar 2022.
Accepted:  25 Octobar 2022.
Published: 25 November 2022.




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.


Keywords – – Ensemble deep learning, neural networks autoencoders, information overload.



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