A DATA-DRIVEN EVALUATION FRAMEWORK FOR ACADEMIC PROMOTION RECOMMENDATION USING STATISTICAL VALIDATION AND REGRESSION ANALYSIS
DOI:
https://doi.org/10.47701/jxxyn485Keywords:
Academic Promotion, Data-Driven Framework, Decision Support, Multiple Linear Regression, Higher EducationAbstract
The academic promotion process in higher education continues to face challenges related to subjectivity, limited transparency, and inconsistencies in evaluating lecturers’ performance. This study develops and evaluates a data-driven framework for academic promotion assessment by integrating multidimensional academic performance indicators with statistical validation and multiple linear regression analysis. A quantitative approach was employed using data collected through a five-point Likert-scale questionnaire from 168 respondents. The research instrument was initially assessed for validity and reliability, followed by classical assumption tests and multiple linear regression analysis to examine the relationships between academic performance indicators and Academic Promotion (AP). The results confirmed that all measurement items were valid and that all constructs demonstrated acceptable internal consistency, with Cronbach’s Alpha coefficients exceeding 0.60. The regression model also satisfied the assumptions of normality, absence of multicollinearity, and homoscedasticity. The overall regression model was statistically significant (F = 3.840, p = 0.001), indicating that the predictors were collectively associated with Academic Promotion. At the individual level, Technological Works (Y6) was the only predictor with a statistically significant positive coefficient (? = 0.157, p = 0.007), whereas Educational Activities, Scientific Publications, Training, Community Service, Committee Involvement, and Scientific Participation were not statistically significant at the 5% level. These findings highlight the distinctive role of technology-oriented and innovation-related outputs in academic promotion while demonstrating the potential of a statistically validated, data-driven framework to support more transparent, consistent, and evidence-based academic promotion assessment in higher education institutions.
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