A Predictive Model using Machine Learning Algorithm in Identifying Student’s Probability on Passing Semestral Course

Abstract

Recommendations -Full automation of prediction results accessible by the students, faculty, and institution administrators for fast management decision making should take place. Further study for the inclusion of some student`s demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed.

Enhancing Student'sPerformance Classification Using Ensemble Modeling

“…They achieved an accuracy of 71.35%. Doctor et al [11] used DT to identify student performance and gain an accuracy of 76.19%. Waheed et al [12] employed the deep LSTM approach to identify student performance with accuracy of 84.57%.…”

“…Author year Method Accuracy [5] 2019 DANN 93 [6] 2020 DM 88.3 [7] 2021 ANN 0.82 [8] 2022 RF, XGBOOST, KNN, SVM 78 [9] 2022 RF, SVM, KNN, LR, NB 86.4 [10] 2023 ANN-LSTM 71.35 [11] 2023 DT 76.19 [12] 2023 DEEP LSTM 84.57 [13] 2023 DT, NB, SVM, NN 87.1 -204 213 [14] 2023…”

“…The study recommended that the fully automated prediction results for rapid decision-making should be considered. In addition, it suggested further research to incorporate demographic data and additional indicators of criteria for student success [11]. Waheed et al (2023) study on self-paced education programs found that the long-term memory deep learning (DL) technique (LSTM) can predict students at risk of failing a course.…”