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All Journal Bulletin of Electrical Engineering and Informatics Asia Pacific Journal of Management and Education
Reyes, Jocelyn L.
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Clustering of students admission data using k-means, hierarchical, and DBSCAN algorithms Cahapin, Erwin Lanceta; Malabag, Beverly Ambagan; Santiago Jr., Cereneo Sailog; Reyes, Jocelyn L.; Legaspi, Gemma S.; Adrales, Karl Louise
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4849

Abstract

Admissions in the university undergo procedures and requirements before a student can be officially enrolled. The senior high school grades remain the most significant in college admission decisions. This paper presents the use of data mining to cluster students based on admission datasets. The admission dataset for 2019-2020 was obtained from the office of student affairs and services. This dataset contains 2,114 observations with 11 attributes. Data preparation and data standardization were performed to ensure that the dataset is ready for processing and implemented in R programming language. The optimal number of clusters (k) was identified using the silhouette method. This method gave an optimal number of k=2 which was used in the actual clustering using the k-means and hierarchical clustering algorithms. Both algorithms were able to cluster students into two: cluster 1-social sciences or board courses and cluster 2-management or non-board courses. Further, density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was also used on the same dataset and it yielded a single cluster. This study can be replicated by using at least a 5-year dataset of students’ admission data employing other algorithms that would suggest students’ retention and turn over to board examinations.
Organizational Influence on the Work Engagement of Instructors in Private Higher Educational Institutions Mendoza, Hermilina A.; Manarpiis, Jane A.; Reyes, Jocelyn L.
Asia Pacific Journal of Management and Education (APJME) Vol 8, No 2 (2025): July 2025
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/apjme.v8i2.3850

Abstract

Organizational factors play an important role in molding work engagement and overall employee performance. This study examines the influence of organizational factors on the work engagement of instructors in private higher education institutions in Cavite, Philippines. Using a quantitative research design, data were collected from 150 instructors via an online survey and analyzed using multiple regression analysis. Results show that interpersonal relationships significantly influence all three dimensions of engagement—physical ß = 0.201, p 0.01), emotional (ß = 0.193, p 0.03), and cognitive (ß = 0.165, p 0.05). Work interaction significantly affects physical (ß = 0.233, p 0.004) and cognitive engagement (ß = 0.178, p 0.03), while task characteristics negatively influence cognitive engagement (ß = -0.229, p 0.01). Organizational norms were found to influence only cognitive engagement (ß = 0.176, p 0.02). The findings highlight that interpersonal relationships are the most consistent predictor of holistic engagement. The study recommends that institutions foster strong interpersonal dynamics to enhance motivation, resilience, and instructional performance. These insights provide actionable guidance for institutional leaders and policymakers seeking to strengthen faculty engagement and improve teaching quality in higher education.
Co-Authors Adrales, Karl Louise Cahapin, Erwin Lanceta Legaspi, Gemma S. Malabag, Beverly Ambagan Manarpiis, Jane A. Mendoza, Hermilina A. Santiago Jr., Cereneo Sailog