Pre-service mathematics teachers' performance prediction using a modified Mann-Tseng forward-backward splitting algorithm

Volume 34, Issue 4, pp 313--325 https://dx.doi.org/10.22436/jmcs.034.04.01

Publication Date: April 02, 2024 Submission Date: January 31, 2024 Revision Date: February 23, 2024 Accteptance Date: March 06, 2024

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Authors

N. Jun-on - Department of Mathematics, Faculty of Science, Lampang Rajabhat University, Lampang 52100, Thailand. P. Peeyada - School of Science, University of Phayao, Phayao 56000, Thailand. R. Suparatulatorn - Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand. - Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand. W. Cholamjiak - School of Science, University of Phayao, Phayao 56000, Thailand.

Abstract

Classifying educational data into a particular category remains challenging due to the massive and extensive number of variables within the dataset. This paper emphasizes a new algorithm for variational inclusion problems with the classification of pre-service mathematics teachers' performance in their method courses through a mathematics teacher education program as its application. First, we propose the modified Mann-Tseng forward-backward splitting algorithm based on inertial technique to speed up the convergence of the algorithm. Then, we prove the weak convergence theorem, we compare and demonstrate the efficacy and applicability of our classification schemes in extreme learning machine (ELM) with other machine learning methods; support vector machine (SVM), logistic regression, boosted trees. Moreover, we compare our algorithm with other algorithms in the same ELM. The application is based on the genuine educational data provided in this paper.

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Keywords

• Variational inclusion problem
• Tseng's method
• data classification
• method course
• pre-service mathematics teacher

MSC

•  47H05
•  47J25
•  49M37
•  97M10
•  97C70
•  97U70

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