Digital Educational Technology for Monitoring and Evaluation: Its Contribution to School Management Effectiveness
DOI:
https://doi.org/10.22437/teknopedagogi.v16i4.56538Keywords:
Accountability, Digital Educational Technology, Monitoring and Evaluation, School Management Effectiveness, Vocational EducationAbstract
Digital transformation in education increasingly requires schools to use technology not only for instructional purposes but also for monitoring, evaluation, and evidence-informed management. This study examined the association between digital educational technology for monitoring and evaluation and school management effectiveness in Muhammadiyah Vocational High Schools in Wonogiri Regency, Indonesia. A quantitative explanatory, cross-sectional design was employed involving 57 educational personnel, including vice principals, heads of vocational study programs, teachers, and administrative staff. Data were collected using a five-point Likert-scale questionnaire comprising 12 items measuring digital monitoring and evaluation and 14 items measuring school management effectiveness. Descriptive statistics, preliminary regression assumption tests, and simple linear regression were conducted using IBM SPSS Statistics 25. The findings showed generally favorable perceptions of both digital monitoring and evaluation and school management effectiveness, with identical overall mean scores of 3.97. Simple linear regression indicated a positive and statistically significant association between digital monitoring and evaluation and school management effectiveness (B = 0.743, β = 0.675, t(55) = 6.781, p < 0.001). The model yielded R² = 0.455 and adjusted R² = 0.445, indicating that 45.5% of the observed variance in school management effectiveness was statistically accounted for by the predictor. These findings suggest that digital monitoring and evaluation can support systematic information management, accountability, and evidence-informed decision-making. However, given the cross-sectional design, the results should be interpreted as statistical associations rather than causal effects.
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