Reframing Educational Human Resource Evaluation in the Algorithmic Age - Pierre Marcello Lopulalan (Journal Evaluation in Education (JEE), 2026)
Purpose of the study: This study aims to reconstruct the concept of educational human resource evaluation (SDMP) in the algorithmic era by examining how digital transformation and data-based governance reshape teacher performance assessment, professional accountability, and institutional decision-making. Methodology: This study employed a mixed-methods approach using an explanatory sequential design. The study involved 55 participants, including teachers, institutional leaders, and digital system developers, selected through purposive sampling from five educational institutions in Indonesia. Quantitative data were obtained from digital performance evaluation records (2022–2024), while qualitative data were collected through semi-structured interviews, document analysis, and direct observation of digital evaluation systems. Quantitative data were analyzed using descriptive statistics and Pearson correlation analysis with SPSS, while qualitative data were analyzed using reflexive thematic analysis. Main Findings: The findings indicate that algorithmic systems enhance transparency and consistency in educator performance measurement, with the adoption rate of digital evaluation systems reaching 82.3% across institutions. A significant positive correlation was found between digital technology use and performance evaluation scores (r = 0.62, p < 0.01). However, the results also reveal that algorithmic evaluation systems tend to prioritize measurable digital activities, potentially overlooking educators' social context, pedagogical depth, and professional values. Novelty/Originality of this study: This study introduces the concept of algorithmic reflexivity as a new evaluative framework in educational management. The framework emphasizes integrating algorithmic analytics with human professional judgment, encouraging educational institutions to critically reflect on the ethical, human-centered, and data-driven implications of algorithmic SDMP evaluation.