IJBTT

Leveraging Applied Machine Learning to Uncover Key Drivers of Engagement and Institutional Trust in Higher Education Ecosystems

Leveraging Applied Machine Learning to Uncover Key Drivers of Engagement and Institutional Trust in Higher Education Ecosystems
	© 2025 by IJETT Journal
	Volume-73 Issue-10
	Year of Publication : 2025
	Author : Daranee Benjateekun, Ploykwan Jedeejit, Wongpanya S. Nuankaew, and Pratya Nuankaew
	DOI : 10.14445/22315381/IJETT-V73I10P123

How to Cite?
Daranee Benjateekun, Ploykwan Jedeejit, Wongpanya S. Nuankaew, and Pratya Nuankaew,"Leveraging Applied Machine Learning to Uncover Key Drivers of Engagement and Institutional Trust in Higher Education Ecosystems", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.289-311, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P123

Abstract
This research formulates a predictive framework to examine the determinants affecting staff engagement and institutional trust in higher education by amalgamating organizational psychology with Explainable Machine Learning(XAI). The sample consisted of 70 academic and administrative personnel from the Faculty of Education and the Faculty of Business Administration at Bangkokthonburi University, chosen via proportionate stratified sampling. A validated questionnaire (Cronbach’s α = 0.95) was employed to assess organizational and motivational factors, as well as engagement and trust. The analysis integrated descriptive and inferential statistics with supervised learning algorithms, such as Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Naïve Bayes. We used cross-validation and standard metrics like accuracy, precision, recall, and F1-score to measure how well the model worked. The results indicated that organizational factors, including leadership support, communication efficacy, and career advancement opportunities, substantially influenced engagement. Intrinsic motivation—comprising autonomy, recognition, and professional development—exerted a more significant influence on institutional trust than extrinsic factors, such as salary or workload. A comparative analysis revealed that Logistic Regression, SVM, and k-NN surpassed other models in predictive accuracy and F1-score. The framework offers a reproducible and ethically robust methodology for HR analytics in higher education, achieving a balance among model efficacy, interpretability, and equity. This helps to meet the Sustainable Development Goals, especially SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth). It shows how important responsible AI is for creating sustainable academic ecosystems.

Keywords
Applied Machine Learning, Organizational Credibility, Educational Data Mining, Trust Modeling, Organizational Trustworthiness, Personnel Engagement.

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