AI-Driven Analysis: Optimizing Tertiary Education Policy through Machine Learning Insights

• Published in: International journal of advances in intelligent informatics Vol. 10; no. 2; pp. 296 - 316
• Main Authors: Sy, Christian Y, Maceda, Lany L, Abisado, Mideth B
• Format: Journal Article
• Language: English
• Published: Yogyakarta Universitas Ahmad Dahlan 01.05.2024
• Subjects: Beneficiaries; bert; bertopic; Collaboration; Curricula; Data mining; Education; Education policy; Effectiveness; Empowerment; Higher education; Interdisciplinary aspects; Machine learning; multiclass text classification; Qualitative analysis; Quality of education; R&D; Refining; Research & development; Student retention; Students; Subject specialists; Success; Support systems; Sustainable development; Text categorization; topic modeling; Tuition; uaqte program; Unsupervised learning; Philippines
• ISSN: 2442-6571; 2442-6571
• DOI: 10.26555/ijain.v10i2.1525

Tertiary education is pivotal in equipping individuals with the necessary knowledge and skills for success, prompting global initiatives for equitable access to quality higher education. The Philippines' Universal Access to Quality Tertiary Education (UAQTE) Act exemplifies this commitment by providing free tertiary education to eligible Filipino students. This study evaluates the UAQTE program's implementation through the perspectives of student beneficiaries, employing a combined approach of qualitative analysis and machine learning techniques. The study utilizes supervised and unsupervised machine learning to analyze student responses, specifically multiclass text classification using BERT and topic modeling with BERTopic. The results reveal insights into students' experiences and perceptions of the UAQTE program. While BERT demonstrates effectiveness in certain categories, challenges such as overfitting and balancing sequence length versus model performance are identified. BERTopic highlights the importance of capturing two-word combinations for enhancing topic coherence. Key themes identified through both approaches include "Educational Opportunity," "Program Implementation," "Financial Support," and "Appreciation and Gratitude," emphasizing their significance within the UAQTE program. Alignment between machine learning analyses and domain experts' insights underscores the relevance and effectiveness of the methodologies employed. Recommendations for optimizing the UAQTE program include refining focus areas, strengthening support systems, incorporating two-word combinations in analysis, and fostering continuous monitoring and interdisciplinary collaboration. By leveraging insights from qualitative and machine learning analyses, administrators can make informed decisions to enhance program effectiveness and comprehensively address students' diverse needs.