Profiling At-Risk Student Characteristics Associated with Mandatory Programmatic Divergence in Licensure-Based Programs at a Private University
DOI:
https://doi.org/10.69569/jip.2026.299Keywords:
At-risk students, Early warning system, Licensure-based programs, Mandatory programmatic divergence, Student attritionAbstract
Student attrition remains a major concern in higher education, particularly in licensure-based programs with rigorous academic standards and retention policies. This study aimed to statistically profile at-risk students associated with mandatory programmatic divergence and provide baseline evidence for the development of institutional retention initiatives and an Early Warning System. The study was conducted at the study institution using a descriptive cross-sectional quantitative research design that combined secondary institutional records and primary survey responses. A total of 220 undergraduate students identified as at risk of mandatory programmatic divergence were selected through proportionate stratified random sampling. The study examined socio-demographic attributes, academic background, institutional retention indicators, and attrition-related factors categorized into academic, financial, and personal-social dimensions. Data were analyzed using frequency counts, percentages, means, standard deviations, the Mann–Whitney U test, and the Kruskal–Wallis H test. The findings showed that most at-risk students were female, belonged to the middle-income group, and were enrolled in the second and third year levels. Academic factors (M = 3.42, SD = 0.88) and personal-social factors (M = 3.46, SD = 0.65) were rated as Frequently Occurred, whereas financial factors (M = 3.08, SD = 1.18) were rated as Sometimes Occurred. Significant differences in academic background were observed across sex, income level, previous course, and present course, while institutional retention indicators differed significantly according to year level. The findings provide a statistical profile of at-risk students in licensure-based programs and offer baseline evidence that may inform institutional retention strategies, student support initiatives, and the future development of an Early Warning System.
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