Unraveling the threads of adaptability: Analyzing key determinants influencing student success in online learning environments

Duongdearn Suwanjinda, Suwimon Kooptiwoot, Chaisri Tharasawatpipat, Sivapan Choo-in, Pantip Kayee, Bagher Javadi

Article ID: 9976
Vol 8, Issue 14, 2024

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Abstract


This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.


Keywords


machine learning algorithms; student adaptability classification; SHAP analysis; online learning environments; adaptability factors

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DOI: https://doi.org/10.24294/jipd9976

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