A Phased Model for Data-Driven Teacher Performance Management Using Educational Analytics in Indonesia

Irmawati Thahir; Nurasia Natsir

doi:10.35445/alishlah.v18i1.9459

A Phased Model for Data-Driven Teacher Performance Management Using Educational Analytics in Indonesia

Irmawati Thahir, Nurasia Natsir

Abstract

Empirical evidence on data-driven teacher performance management in resource-constrained contexts remains limited, particularly in Eastern Indonesia. This study examines the transition from traditional to data-driven performance management in secondary schools in Makassar City. A concurrent embedded mixed-methods design was employed, prioritizing quantitative data with qualitative insights to explain contextual dynamics. The sample comprised 113 teachers (94.2% response rate) and 15 principals from 15 purposively selected schools categorized as adopters, transitional, or traditional. Teacher performance was measured across four domains: lesson planning, instructional delivery, assessment, and professional conduct. Validated questionnaires (α > 0.87; CFI = 0.96, RMSEA = 0.048) were analyzed using t-tests and ANOVA with effect sizes and assumption checks. Qualitative data from interviews, focus groups, and observations were thematically analyzed and integrated. Traditional approaches predominated (67%). Adopter schools (33%) scored significantly higher across all performance management dimensions (p < 0.001). Teachers in adopter schools demonstrated a 23% higher mean performance score over one academic year (F = 18.45, p < 0.001, η² = 0.25). Key enabling factors included leadership, digital infrastructure, and data literacy, while barriers comprised resistance to change, budget limitations, and competency gaps. Although findings are associational due to the cross-sectional design, results suggest that data-driven systems are linked to improved teacher performance. The study proposes a phased implementation model—awareness, capacity building, piloting, scaling, and institutionalization—tailored to resource-constrained settings.

Keywords

teacher performance management; data-driven performance management; educational analytics; performance measurement; digital transformation

References

Almubarak, A., Alhalabi, W., Albidewi, I., & Alharbi, E. (2024). An analytical approach for an AI-based teacher performance evaluation system in Saudi Arabia’s schools. Discover Applied Sciences, 6, 406. https://doi.org/10.1007/s42452-024-06117-4

AlZoubi, D., & Baran, E. (2024). A closer look at instructor use and sensemaking processes of analytics dashboards: Past, present, and future. Journal of Learning Analytics, 11(2). https://doi.org/10.18608/jla.2024.7961S

Amarasinghe, I., Michos, K., Crespi, F., & Hernández-Leo, D. (2022). Learning analytics support for teachers’ design and orchestration decisions. Journal of Computer Assisted Learning, 38(6), 2416–2431. https://doi.org/10.1111/jcal.12711

Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, 100489. https://doi.org/10.1016/j.edurev.2022.100489

Beck, J. S., & Nunnaley, D. (2021). A continuum of data literacy for teaching. Studies in Educational Evaluation, 69, 100871. https://doi.org/10.1016/j.stueduc.2020.100871

Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis. Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238

Copur-Gencturk, Y., Li, J., Cohen, A. S., & Orrill, C. H. (2024). The impact of an interactive, personalized computer-based teacher professional development program on student performance: A randomized controlled trial. Computers & Education, 210, 104963. https://doi.org/10.1016/j.compedu.2023.104963

Datnow, A., & Hubbard, L. (2022). Teacher capacity for and beliefs about data-driven decision making: A literature review of international research. Journal of Educational Change, 23(1), 7–35. https://doi.org/10.1007/s10833-021-09422-2

Datnow, A., Park, V., & Wohlstetter, P. (2021). Achieving data use for continuous improvement: A distributed leadership perspective. Educational Management Administration & Leadership, 49(2), 239–259. https://doi.org/10.1177/1741143220902213

Fütterer, T., Hou, R., Bühler, B., Bozkir, E., Kasneci, E., Gerjets, P., & Trautwein, U. (2026). Validating automated assessments of teaching effectiveness using multimodal data. Learning and Instruction, 101, 102264. https://doi.org/10.1016/j.learninstruc.2025.102264

Gašević, D., Tsai, Y.-S., Dawson, S., & Pardo, A. (2022). How do we start? An approach to learning analytics adoption in higher education. International Journal of Information and Learning Technology, 39(4), 342–363. https://doi.org/10.1108/IJILT-02-2022-0041

Hardiansyah, F., AR, M. M., & Hidayatillah, Y. (2022). IPAS Learning Assessment To Measure Science Process Skill In Elementary School. International Journal of Elementary Education, 6(3), 612–623. https://doi.org/https://doi.org/10.23887/ijee.v6i4.54217

Hardiansyah, F., Zainuddin, Z., Sukitman, T., & Astutik, C. (2023). Development Of Learning Media Smart Book To Improve Understanding Of Elementary School Students In Science Learning. Lentera Pendidikan: Jurnal Ilmu Tarbiyah Dan Keguruan, 26(1), 72–87. https://doi.org/https://doi.org/10.24252/lp.2023v26n1i7

Kane, T. J., & Staiger, D. O. (2023). Gathering feedback for teaching: Combining high-quality observations with student surveys and achievement gains. Educational Evaluation and Policy Analysis, 45(1), 85–111. https://doi.org/10.3102/01623737221120635

Kraft, M. A., & Gilmour, A. F. (2023). Revisiting the widget effect: Teacher evaluation reforms and the distribution of teacher effectiveness. Educational Researcher, 52(1), 5–20. https://doi.org/10.3102/0013189X221137217

Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842. https://doi.org/10.1016/j.stueduc.2020.100842

Marsh, J. A., Farrell, C. C., & Bertrand, M. (2022). Trickle-down accountability: How middle school teachers engage students in data use. Educational Policy, 36(1), 95–125. https://doi.org/10.1177/0895904814531653

Michaud, L., & Schildkamp, K. (2024). Dimensions of teachers’ data literacy: A systematic review. Educational Research Review, 41, 100548. https://doi.org/10.1016/j.edurev.2023.100548

Nguyen, N. B. C., Lithander, M., Östlund, C. M., Karunaratne, T., & Jobe, W. (2024). TEADASH: Implementing and evaluating a teacher-facing dashboard using design science research. Informatics, 11(3), 61. https://doi.org/10.3390/informatics11030061

Ordoñez-Avila, R., Salgado Reyes, N., Meza, J., & Ventura, S. (2023). Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review. Heliyon, 9(3), e13939. https://doi.org/10.1016/j.heliyon.2023.e13939

Popham, W. J. (2021). Classroom assessment: What teachers need to know. Pearson Education.

Schelling, N., & Rubenstein, L. D. (2021). Teachers’ perceptions of data-driven decision making. Educational Assessment, Evaluation and Accountability, 33, 317–344. https://doi.org/10.1007/s11092-021-09356-w

Schildkamp, K., Poortman, C. L., Luyten, H., & Ebbeler, J. (2021). Factors promoting and hindering data-based decision making in schools. School Effectiveness and School Improvement, 32(2), 242–262. https://doi.org/10.1080/09243453.2020.1830114

Siemens, G., & Long, P. (2021). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 56(5), 30–40.

Steinberg, M. P., & Garrett, R. (2022). Classroom observations in context: A mixed-methods investigation of the factors that matter for observation accuracy. Educational Evaluation and Policy Analysis, 44(1), 3–28. https://doi.org/10.3102/01623737211034290

Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboards: Providing actionable insights to teachers and learners. International Journal of Educational Technology in Higher Education, 19, 12. https://doi.org/10.1186/s41239-021-00313-7

Tan, X., Cheng, G., & Ling, M. H. (2024). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers & Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355

Visscher, A. J., & Ehren, M. C. M. (2021). The simplicity–complexity paradox in data-based decision making. Educational Management Administration & Leadership, 49(5), 743–761. https://doi.org/10.1177/1741143220911263

DOI: https://doi.org/10.35445/alishlah.v18i1.9459