Narratives of Developing Authentic Assessment Instruments for Islamic Religious Education in Secondary Schools Through a Participatory ADDIE Approach
Indonesia Indonesia Indonesia 
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Authentic Assessment, Islamic Religious Education, Participatory Module
Abstract
Background. Authentic assessment has become a central component of the Merdeka Curriculum implementation in Indonesia, particularly in Islamic Religious Education (PAI), which emphasizes holistic student development across cognitive, affective, and psychomotor domains.
Purpose. This study aimed to develop a valid and practical participatory-based module for developing authentic assessment instruments in secondary school PAI learning through the integration of the ADDIE model and participatory professional collaboration within the MGMP forum.
Method. This study employed a Research and Development (R&D) design using the ADDIE model consisting of Analysis, Design, Development, Implementation, and Evaluation phases. The research involved 35 PAI teachers from 23 senior secondary schools in Nagan Raya Regency, Aceh Province.
Results. The findings revealed that the developed module achieved an average expert validity score of 85% categorized as Very Valid and an average practicality score of 80% categorized as Practical. The participatory mechanism implemented through structured FGDs effectively positioned teachers and supervisors as active co-constructors in developing authentic assessment instruments that were contextual, relevant, and applicable to classroom practice.
Conclusion. The integration of the ADDIE model with a participatory approach provides an effective framework for developing authentic assessment competencies among PAI teachers.
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References
Broadbent, J. (2023). Beyond emergency remote teaching: Did the pandemic lead to lasting change in university courses? International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00428-z
Cai, M. (2022). Boosted photocatalytic antibiotic degradation performance of Cd0.5Zn0.5S/carbon dots/Bi2WO6 S-scheme heterojunction with carbon dots as the electron bridge. Separation and Purification Technology, 300(Query date: 2026-06-05 15:31:23). https://doi.org/10.1016/j.seppur.2022.121892
Gao, G. (2023). CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM. Sensors, 23(18). https://doi.org/10.3390/s23187714
Heidari, A. (2024). Deepfake detection using deep learning methods: A systematic and comprehensive review. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 14(2). https://doi.org/10.1002/widm.1520
Hou, R. (2024). Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14829(Query date: 2026-06-05 15:31:23), 60–74. https://doi.org/10.1007/978-3-031-64302-6_5
Hsiao, Y. P. (2023). Developing a framework to re-design writing assignment assessment for the era of Large Language Models. Learning Research and Practice, 9(2), 148–158. https://doi.org/10.1080/23735082.2023.2257234
James, M. (2022). Collaborative case-based learning with programmatic team-based assessment: A novel methodology for developing advanced skills in early-years medical students. BMC Medical Education, 22(1). https://doi.org/10.1186/s12909-022-03111-5
Jiang, T. (2022). Awe Motivates Authentic-Self Pursuit via Self-Transcendence: Implications for Prosociality. Journal of Personality and Social Psychology, 123(3), 576–596. https://doi.org/10.1037/pspi0000381
Lam, R. (2023). E-Portfolios: What We Know, What We Don’t, and What We Need to Know. Relc Journal, 54(1), 208–215. https://doi.org/10.1177/0033688220974102
Lavoie, J. A. A. (2022). Developing Community Co-designed Scenario-Based Training for Police Mental Health Crisis Response: A Relational Policing Approach to De-escalation. Journal of Police and Criminal Psychology, 37(3), 587–601. https://doi.org/10.1007/s11896-022-09500-2
Li, B. (2022). Blindly Assess Quality of In-The-Wild Videos via Quality-Aware Pre-Training and Motion Perception. IEEE Transactions on Circuits and Systems for Video Technology, 32(9), 5944–5958. https://doi.org/10.1109/TCSVT.2022.3164467
Li, L. (2022). Blind Image Quality Index for Authentic Distortions with Local and Global Deep Feature Aggregation. IEEE Transactions on Circuits and Systems for Video Technology, 32(12), 8512–8523. https://doi.org/10.1109/TCSVT.2021.3112197
Mate, K. (2022). Considerations and strategies for effective online assessment with a focus on the biomedical sciences. FASEB Bioadvances, 4(1), 9–21. https://doi.org/10.1096/fba.2021-00075
Pan, Z. (2022). DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network. IEEE Transactions on Circuits and Systems for Video Technology, 32(11), 7518–7531. https://doi.org/10.1109/TCSVT.2022.3188991
Perkins, D. (2022). Changes in mental health, wellbeing and personality following ayahuasca consumption: Results of a naturalistic longitudinal study. Frontiers in Pharmacology, 13(Query date: 2026-06-05 15:31:23). https://doi.org/10.3389/fphar.2022.884703
Revell, T. (2024). ChatGPT versus human essayists: An exploration of the impact of artificial intelligence for authorship and academic integrity in the humanities. International Journal for Educational Integrity, 20(1). https://doi.org/10.1007/s40979-024-00161-8
Salinas-Navarro, D. E. (2024). Designing experiential learning activities with generative artificial intelligence tools for authentic assessment. Interactive Technology and Smart Education, 21(4), 708–734. https://doi.org/10.1108/ITSE-12-2023-0236
Shi, J. (2024). Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token. IEEE Transactions on Multimedia, 26(Query date: 2026-06-05 15:31:23), 4641–4651. https://doi.org/10.1109/TMM.2023.3325719
Song, T. (2022). Blind Image Quality Assessment for Authentic Distortions by Intermediary Enhancement and Iterative Training. IEEE Transactions on Circuits and Systems for Video Technology, 32(11), 7592–7604. https://doi.org/10.1109/TCSVT.2022.3179744
Southwell, R. (2022). Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms. Proceedings of the 15th International Conference on Educational Data Mining Edm 2022, (Query date: 2026-06-05 15:31:23). https://doi.org/10.5281/zenodo.6853109
Sun, W. (2023). Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training. IEEE Journal on Selected Topics in Signal Processing, 17(6), 1178–1192. https://doi.org/10.1109/JSTSP.2023.3270621
Walland, E. (2022). E-portfolios in teaching, learning and assessment: Tensions in theory and praxis. Technology Pedagogy and Education, 31(3), 363–379. https://doi.org/10.1080/1475939X.2022.2074087
Winstone, N. E. (2022). Discipline-specific feedback literacies: A framework for curriculum design. Higher Education, 83(1), 57–77. https://doi.org/10.1007/s10734-020-00632-0
Zacometti, C. (2024). Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning. Food Research International, 179(Query date: 2026-06-05 15:31:23). https://doi.org/10.1016/j.foodres.2024.114023
Zhou, J. (2022). Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction. IEEE Transactions on Circuits and Systems for Video Technology, 32(5), 2535–2549. https://doi.org/10.1109/TCSVT.2021.3063952
