Universal Journal of Educational Research Vol. 10(7), pp. 448 - 458
DOI: 10.13189/ujer.2022.100703
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Machine Learning Ecosystem to Enhance Grade Point Average

Sasitorn Issaro ^1,*, Pallop Piriyasurawong ^2
1 Faculty of Industrial Technology, Nakhon Si Thammarat Rajabhat University, Thailand
² Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

ABSTRACT

This study aims to 1) develop Machine Learning Ecosystem models to enhance grade point averages, and 2) predict grade point averages by modeling Machine Learning Ecosystem techniques, namely, Decision Trees (DT), Naïve Bayes (NB), and a Neural Network (NN). Findings from an efficiency comparison of the three models of a Machine Learning Ecosystem in predicting grade point averages showed that DT achieved the highest accuracy of 100.00%. In contrast, NN achieved the second-highest accuracy of 85.83%, and NB achieved the lowest accuracy of 81.67%. For the F-Measure, the F-Measure with DT, NN, and NB was 100.00%, 76.59%, and 70.59%, respectively. Thus, DT was the most appropriate model for a Machine Learning Ecosystem to predict the students' GPA.

KEYWORDS
Learning Ecosystem, Machine Learning, Grade Point Average, Decision Tree, Naïve Bayes, Neural Network

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Sasitorn Issaro , Pallop Piriyasurawong , "Machine Learning Ecosystem to Enhance Grade Point Average," Universal Journal of Educational Research, Vol. 10, No. 7, pp. 448 - 458, 2022. DOI: 10.13189/ujer.2022.100703.

(b). APA Format:
Sasitorn Issaro , Pallop Piriyasurawong (2022). Machine Learning Ecosystem to Enhance Grade Point Average. Universal Journal of Educational Research, 10(7), 448 - 458. DOI: 10.13189/ujer.2022.100703.