Sustaining Students’ Quality Learning Environment by Reviewing Factors to Graduate-on-Time: A case study

Sidhu, Gurnam Kaur; Kannan, Sathiamoorthy; Shamida, Arieff; Du, Ruofei

doi:10.21834/ebpj.v8i24.4649

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Students feel more motivated and engaged in the classroom and connected to their lecturers and peers when they have positive emotional well-being. Students' motivation has been found to influence their ability to complete their studies (Sidhu et al, 2023). They are more willing to participate and contribute during the learning process, which leads to better learning experiences, thus contributing to better students' academic achievement (Buari & Alim, 2020).…”

Section: Mindframe 7: My Lecturer Develops Positive Relationshipsmentioning

confidence: 99%

Perspectives of Visible Learning for Quality Learning Environment: A Case Study

Tong,

Sidhu,

Wang

2023

E-BPJ

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A quality learning environment calls for learning to be visible for both lecturers and students. This study aimed to investigate the students' perspectives of the Ten Mindframes and the predictor mindframes of Visible Learning, which significantly contribute to students' academic achievement. Using a mixed-methods research approach, this study involved 416 students in undergraduate programmes. The findings revealed students valued the constructivism construct and the social-emotional aspects of Visible Learning mindframes. The results suggest that a more significant consolidation effort is required to promote a quality learning environment through visible teaching and learning.

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Section: Mindframe 7: My Lecturer Develops Positive Relationshipsmentioning

confidence: 99%

Perspectives of Visible Learning for Quality Learning Environment: A Case Study

Tong,

Sidhu,

Wang

2023

E-BPJ

Self Cite

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“…Students who graduate on time (GOT) are those who completed their studies timely within the time frame specified by the university [1,2]. Its significance extends beyond individual achievement, serving as a metric for evaluating institutional quality and performance [1,3].…”

Section: Introductionmentioning

confidence: 99%

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Law,

Ting,

et al. 2024

JIWE

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In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i) compare various class imbalance treatment methods with different sampling ratios, (ii) propose an ensemble class imbalance treatment method in mitigating the problem of class imbalance, and (iii) develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university. The dataset is collected from 4007 graduates of a university from year 2021 and 2022 with 41 variables. After feature selection, various class imbalance treatment methods were compared with different sampling ratios ranging from 50% to 90%. Moreover, Ensemble-SMOTE is proposed to aggregate the dataset generated by Synthetic Minority Oversampling Technique variants in mitigating the problem of class imbalance effectively. The dataset generated by class imbalance treatment methods were used as the input of the predictive models in detecting on-time graduation. The predictive models were evaluated based on accuracy, precision, recall, F0.5-score, F1-score, F2-score, Area under the Curve, and Area Under the Precision-Recall Curve. Based on the findings, Logistic Regression with Ensemble-SMOTE outperformed other predictive models, and class imbalance treatment methods by achieving the highest average accuracy (87.24), recall (92.50%), F1-score (91.30%), and F2-score (92.02%) from 6th until 10th trimester. To assess the effectiveness of class imbalance treatment methods, Friedman test is performed to determine on significant difference between the models after applying Shapiro-Wilk test in normality test. Consequently, Ensemble-SMOTE is ranked as the top-performers by achieving the lowest value in the average rank based on the performance metrics. Additional research could incorporate and examine more complicated approaches in mitigating class imbalance when the dataset is highly imbalanced.

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