Abstract
The processing and interpretation of evaluation scores is a crucial step in learning assessment because it determines the validity of information regarding student learning outcomes. Technological developments have driven significant changes in evaluation data processing techniques, from manual approaches to the use of digital technology. This article aims to examine manual and digital evaluation score processing techniques and to analyze the process of interpreting evaluation results systematically and meaningfully. The method used is a literature study with a qualitative descriptive approach through a review of various textbooks, journal articles, and scientific documents relevant to educational evaluation and the use of assessment technology. The results of the study indicate that digital-based evaluation data processing has advantages in terms of efficiency, accuracy, and ease of analysis compared to manual techniques, although the manual approach is still relevant under certain conditions. Appropriate analysis and interpretation of evaluation results can help educators understand the level of learning achievement, identify remedial or enrichment needs, and support objective learning decision-making. Thus, mastery of evaluation score processing and interpretation techniques is an important competency for educators in improving the quality of assessment and the learning process.
References
Anghel, E., Khorramdel, L., & von Davier, M. (2024). The use of process data in large-scale assessments: A literature review. Large-scale Assessments in Education, 12(1), 13. https://doi.org/10.1186/s40536-024-00202-1
Anisah, G. (2024). Digitalisasi evaluasi dan implikasinya terhadap ketepatan pengukuran. Jurnal Pengukuran Pendidikan dan Pembelajaran Digital, 9(1), 90–96.
Arbeni, W. (2024). The importance of educational evaluation in the teaching and learning process at madrasah aliyah ishlahiyah Binjai. Holistic Science, 4(3), 592-596. https://doi.org/10.56495/hs.v4i3.826
Hase, A., & Kuhl, P. (2024). Teachers’ use of data from digital learning platforms for instructional design: a systematic review. Educational technology research and development, 72(4), 1925-1945. https://doi.org/10.1007/s11423-024-10356-y
Rizbudiani, A. D., Jaedun, A., Rahim, A., & Nurrahman, A. (2021). Rasch model item response theory (IRT) to analyze the quality of mathematics final semester exam test on system of linear equations in two variables (SLETV). Al-Jabar: Jurnal Pendidikan Matematika, 12(2), 399-412. https://doi.org/10.24042/ajpm.v12i2.9939
Roza, A. S., Dewi, A. F., & Wahyuni, S. (2024). Digital-based learning evaluation model for high school students. Jurnal Paedagogy, 11(4), 727-736. https://doi.org/10.33394/jp.v11i4.12826
Santoso, A., Pardede, T., Djidu, H., Apino, E., Rafi, I., Rosyada, M. N., & Abd Hamid, H. S. (2022). The effect of scoring correction and model fit on the estimation of ability parameter and person fit on polytomous item response theory. REID (Research and Evaluation in Education), 8(2), 140-151. https://doi.org/10.21831/reid.v8i2.54429
Sari, P. D. (2023). Analisis hasil evaluasi pembelajaran berbasis digital di era transformasi pendidikan. Jurnal Teknologi dan Pembelajaran Abad 21, 4(2), 110–118.
Zaenal, A. (2016). Evaluasi Pembelajaran (Prinsip, Teknik, dan Prosedur). Rosda Karya

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