Development of a Deep Learning Assessment Instrument in Senior High School Chemistry Education Using the Rasch Model to Support SDG 4: Quality Education
DOI:
10.29303/jppipa.v12i4.14841Published:
2026-04-25Downloads
Abstract
This study aims to develop a valid and reliable assessment instrument for the application of deep learning in high school chemistry instruction. The development of the instrument is based on Wilson’s Four Building Blocks approach (2005), which includes construct maps, items, item scores, and measures. The study was conducted with 271 twelfth-grade students from three public high schools in West Sumatra, selected through purposive sampling. Data were collected using a student perception questionnaire designed based on the four pillars of deep learning implementation: graduate profile dimensions, learning principles, learning experiences, and learning frameworks. Content validity was analyzed using Aiken’s V, while empirical validity and reliability were tested using the Rasch Model. The results showed an average Aiken’s V value of 0.90, indicating excellent content validity. Rasch analysis yielded a respondent reliability of 0.97 and an item reliability of 0.95, with fit statistics within the ideal range and supporting construct unidimensionality. From the initial 116 items, the final instrument consists of 46 items that passed through a rigorous empirical selection process to ensure high measurement quality. Thus, this instrument is suitable for evaluating the level of deep learning implementation at the elementary to intermediate levels in high school chemistry education.
Keywords:
Deep learning Guided discovery learning Joyful learning Meaningful learning Rasch modelReferences
Amsor, A., Saputra, M. R. D., Nurdini, N., Asri, F. N., & Almaidah, S. (2025). Analytical Thinking Abilities in Physics Education Students: A Rasch-Based Diagnostic Study. Jurnal Penelitian Pendidikan IPA, 11(12), 640–651. https://doi.org/10.29303/jppipa.v11i12.13444
Araújo, L. R., Neto, J. E. S., Silva, J. R. R. T., & Leite, B. S. (2024). Digital Technologies in Chemistry Teaching in the Journal Chemistry Education Research and Practice: an analysis through a Systematic Literary Review. Latin American Journal of Science Education, 11(12001), 1–17. Retrieved from https://www.lajse.org/may24/2024_12001.pdf
Ariani, D., & Bayti, T. N. (2020). Gagasan Millenial & Generasi Z Untuk Indonesia Emas 2045. Fianosa Publishing.
Avcu, A. (2025). The Contribution of Rasch Modeling on Measuring Attitudes For Better Classroom Assessment. International Journal of Psychology and Educational Studies, 12(3), 169–179. https://doi.org/10.52380/ijpes.2025.12.3.1432
Avinç, E., & Doğan, F. (2024). Digital literacy scale: Validity and reliability study with the rasch model. Education and Information Technologies, 29(17). https://doi.org/10.1007/s10639-024-12662-7
Awaliyah, C., Oktaviana, D., & Herlambang, Y. T. (2024). Challenges and Opportunities of Technology in the Dynamics of Life in the Technological Era. UPGRADE: Journal of Information Technology Education, 1(2), 91–96. https://doi.org/10.30812/upgrade.v1i2.3729
Bahsoan, A., Tine, N., Santoso, I. R., Ibrahim, N. L., & Pupala, B. (2025). Rasch-Based Cognitive Diagnostic Assessment to Measure Critical Thinking Skills in Islamic Religious Education. Iqra Journal of Educational Studies, 10(3), 1–17. https://doi.org/10.25217/ji.v10i3.6648
Beers, S. Z. (2011). 21st Century Skills: Rethinking How Students Learn. International Society for Technology in Education.
Biggs, J. (2011). Teaching for Quality Learning at University. UK: SRHE and Open University Press.
Bordunos, A. K., Miletich, M. P., & Volkova, N. V. (2024). Mindful Learning: Principles and Prospect of Use in Higher Education. Psychological Science and Education, 29(4), 16–30. https://doi.org/10.17759/pse.2024290402
Bråten, O. M. H., & Skeie, G. (2020). ‘ Deep Learning ’ in Studies of Religion and Worldviews in Norwegian Schools? The Implications of the National Curriculum Renewal in 2020. MDPI, 11(April), 1–16. https://doi.org/10.3390/rel11110579
Costa, A. L., & Kallick, B. (2009). Habits of mind across the curriculum : practical and creative strategies for teachers. Association for Supervision and Curriculum Development.
Cronqvist, M. (2024). Enhanced student joy in learning environment; understanding and influencing the process. European Journal of Education, 59, 1–12. https://doi.org/10.1111/ejed.12671
Dewi, S. L., Kurniawan, D. T., & Sukardi, R. R. (2023). Unravelling Misconceptions: Analising Primary School Pupils’ Understanding on the Greenhouse Effect through the RASCH Model . Jurnal Penelitian Pendidikan IPA, 9(11), 9353–9362. https://doi.org/10.29303/jppipa.v9i11.5499
Dong, Y., Xu, W., Huang, J., & Yann, K. (2025). Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model. Humanities And Social Sciences Communications, 12(1317), 1–13. https://doi.org/10.1057/s41599-025-05670-6
Fakhrudin, I. A., Utami, B., Indriyanti, N. Y., Khasanah, A. N., Probosari, R. M., Supurwoko, & Permatasari, E. (2024). Assessing Self-Reliance among Generation Z in Junior High School: An Analysis Using Rasch Model. Jurnal Penelitian Pendidikan IPA, 10(2), 619–624. https://doi.org/10.29303/jppipa.v10i2.6010
Fawzi, F. R., Kadarsyah, D., & Sutarsih, C. (2024). Transformation of Educational Institutions: The Role of Independent Learning in Effective Management Practices. Didaktika: Journal of Education, 13(001), 139–154. https://doi.org/10.58230/27454312.1533
Feri, M., Ismiati, N., Al-nur, W. R., & Akbar, F. N. (2025). Implementing Deep Learning Approaches in Primary Education : A Literature Review. VARIDIKA Journal, 37(1), 178–194. https://doi.org/10.23917/varidika.v37i2.12151
Feriyanto, F., & Anjariyah, D. (2024). Deep Learning Approach Through Meaningful , Mindful , and Joyful Learning : A Library Research. Electronic Journal of Education, Social Economic and Technology, 5(2), 208–212. https://doi.org/10.33122/ejeset.v5i2.321
Fullan, M., Quinn, J., & Mceachen, J. (2018). Deep Learning: Engage the world change the world. SAGE.
Griffin, P., & Care, E. (2015). Assessment and Teaching of 21st Century Skills Methods and Approach. Springer
Hakim, A. N., & Yulia, L. (2024). The Impact of Digital Technology on Education Today. Pediaqu: Journal of Social and Humanities Education, 3(1), 145–163. Retrieved from https://publisherqu.com/index.php/pediaqu/article/view/800/728
Handa, V. C., & Talisayon, V. M. (2023). Practical problem-solving tasks for meaningful learning and retention in college chemistry for pre-service teachers. European Journal of Science and Mathematics Education, 11(4), 702–716. https://doi.org/10.30935/scimath/13497
Hattie, J. A. C., & Donoghue, G. M. (2016). Learning strategies: a synthesis and conceptual model. Science of Learning, 1. https://doi.org/10.1038/npjscilearn.2016.13
Hermes, J., & Rimanoczy, I. (2018). The International Journal of Deep learning for a sustainability mindset. The International Journal of Management Education, 16, 460–467. https://doi.org/10.1016/j.ijme.2018.08.001
Irma, D., & Suparto. (2025). The Role of Accreditation in Improving Higher Education Quality: A Policy Review in Indonesia. IHSAN: Journal of Islamic Education, 3(3), 513–520. https://doi.org/10.61104/ihsan.v3i3.1264
Jeet, G., & Pant, S. (2023). Creating Joyful Experiences for Enhancing Meaningful Learning and Integrating 21 st Century Skills. International Journal of Current Science Research and Review, 06(02), 900–903. https://doi.org/10.47191/ijcsrr/V6-i2-05
Julfan, I., & Haifaturrahmah, H. (2025). The Impact of Gadget Use on Student Learning Concentration. Action Research Journal Indonesia (ARJI), 7(4), 3310–3326. https://doi.org/10.61227/arji.v7i4.621
Karoror, I., & Jalmo, T. (2022). Profile of Critical Thinking Ability in Ecosystem Materials using the Rasch Model. Jurnal Penelitian Pendidikan IPA, 8(3), 1599–1604. https://doi.org/10.29303/jppipa.v8i3.1394
Khamboonruang, A. (2025). Argument ‑ based validation of Chulalongkorn University Language Institute (CULI) test : a Rasch ‑ based evidence investigation. Language Testing in Asia, 15(10), 1–24. https://doi.org/10.1186/s40468-025-00346-z
Kusnadi, N. F., & Sukmawati, W. (2023). Analysis of Changes in the Level of Difficulty of Elementary School Students in Learning the RADEC Model on the Concept of Energy Transformation Using the Rasch Model. Jurnal Penelitian Pendidikan IPA, 9(SpecialIssue), 1121–1127. https://doi.org/10.29303/jppipa.v9iSpecialIssue.4036
Lestari, I., Haslan, M. M., & Sawaludin, S. (2025). P The Application of Law Number 20 of 2003 Concerning the National Education System in Improving the Learning Process at SMAN 1 Tanjung. Development Innovation: Kelitbangan Journal, 13(2), 1–16. Retrieved from https://jurnal.balitbangda.lampungprov.go.id/index.php/jip/article/view/1245
Liu, X. (2020). Using and developing measurement instruments in science education: a Rasch modeling approach 2nd edition. IAP. Retrieved from https://eric.ed.gov/?id=ED510146
Manik, A. C., Sutrisno, H., & Rivaldo, I. (2022). Testing the Validity and Reliability of Conceptual Chemistry Learning Instruments during the COVID-19 Pandemic Using Rasch Modeling. Jurnal Penelitian Pendidikan IPA, 8(3), 1561–1568. https://doi.org/10.29303/jppipa.v8i3.1486
Matondang, K., Lubis, U. A., Syahputri, N., Saragih, S., Saragih, R. M. B., Azis, Z., & Nisa, R. K. (2025). Integration of Technology and Local Wisdom in Geometry Learning. Pustaka Media Publishing.
Merkebu, J., Kitsantas, A., Durning, S. J., & Ma, T. (2023). What is metacognitive reflection ? The moderating role of metacognition on emotional regulation and reflection. Frontiers in Education, 8(1166195), 1–14. https://doi.org/10.3389/feduc.2023.1166195
Metsämuuronen, J. (2023). Seeking the real item difficulty : bias ‑ corrected item difficulty and some consequences in Rasch and IRT modeling. In Behaviormetrika (Vol. 50). Springer Japan. https://doi.org/10.1007/s41237-022-00169-9
Miranda, I., Campos, R., Melo-Ferreira, J., Ferreira, M. N. P., & Marques, J. P. (2025). Promoting Scientific Literacy in High School : Insights from Short-Term STEM Collaborative Projects. European Journal of STEM Education, 10(1), 1–18. https://doi.org/10.20897/ejsteme/17167
Mokkink, L. B., Herbelet, S., Tuinman, P. R., & Terwee, C. B. (2025). Content validity : judging the relevance, comprehensiveness, and comprehensibility of an outcome measurement instrument e a COSMIN perspective. Journal of Clinical Epidemiology, 185, 111879. https://doi.org/10.1016/j.jclinepi.2025.111879
Morell, L., Bathia, S., Koo, B. W., Wilson, M., Gochyyev, P., & Smith, R. (2025). Developing and Gathering Validity Evidence for an Instrument to Measure How High School Students Identify as Researchers. Research in Science Education, 55, 359–382. https://doi.org/10.1007/s11165-024-10194-1
Muliani, M., Novita, N., Mellyzar, M., Pasaribu, A. I., & Fadli, M. R. (2022). Analysis of the Characteristics of the Ethnoscience-Based Numeracy Test Instrument Using the Rasch Model. Jurnal Penelitian Pendidikan IPA, 8(5), 2176–2183. https://doi.org/10.29303/jppipa.v8i5.2285
Mulyanti, S., Suwahono, S., Setiowati, H., & Ningrum, L. S. (2022). Validity Analysis Using the Rasch Model in the Development of Alkane Concept Test Instruments. Jurnal Penelitian Pendidikan IPA, 8(3), 1142–1147. https://doi.org/10.29303/jppipa.v8i3.1383
Nurhakim, H. Q., Rojibillah, I., Harsing, H., Supiana, S., & Zakiah, Q. Y. (2025). Curriculum Innovation and Learning Technology (Deep Learning). Journal of Education & Learning Technology, 6(2), 134–143. https://doi.org/10.37859/eduteach.v6i02.9487
Oskarita, E., & Arasy, H. N. (2024). The Role of Digital Tools in Enhancing Collaborative Learning in Secondary Education. International Journal of Educational Research, 1(1), 26–32. https://doi.org/10.62951/ijer.v1i1.15
Risqianto, F., Fadlullah, M., & Hanif, M. (2025). Changes in Learning Motivation in the Era of Digital Learning and Its Influence on Students' Academic Interest. Academic Media Journal (JMA), 3(12), 1–16. Retrieved from https://jurnal.mediaakademik.com/index.php/jma/article/view/3771
Safitri, A. O., Yunianti, V. D., & Rostika, D. (2022). Efforts to Improve Quality Education in Indonesia: Analysis of Sustainable Development Goals (SDGs) Achievement. Basicedu Journal, 6(4), 7096–7106. https://doi.org/10.31004/basicedu.v6i4.3296
Sjöström, J., Yavuzkaya, M., Guerrero, G., & Eilks, I. (2024). Critical Chemical Literacy as a Main Goal of Chemistry Education Aiming for Climate Empowerment and Agency. Journal of Chemical Education, 101, 4189−4195. https://doi.org/10.1021/acs.jchemed.4c00452
Suparwati, N. M. A. (2022). Analysis of Chemical Misconception Reduction with a Multi-Level Representation Approach: Systematic Literature Review. Journal of Mathematics and Natural Sciences Education, 12(2), 341–348. https://doi.org/10.37630/jpm.v12i2.591
Suryandari, K. C., Wijayanti, M. D., & Wahyudi. (2023). Using The Rasch Model to Measure Prospective Elementary Teachers’ Perception toward Sciencepreneurship, Creative Thinking, Career Adaptability, and Personal Goals. Jurnal Penelitian Pendidikan IPA, 10(7), 3975–3982. https://doi.org/10.29303/jppipa.v10i7.7381
Taherdoost, H. (2021). Data Collection Methods and Tools for Research ; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects. International Journal of Academic Research in Management (IJARM), 10(1), 1–30. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4178676
Talanquer, V. (2026). The Importance of Fostering Conceptual Reframing in Chemistry Education. Chemistry Education Research and Practice, 1–9. https://doi.org/10.1039/D5RP00421G
Taridala, S., & Anwar, R. (2023). Transformasi edukasi: mengoptimalisasi kinerja guru dan kualitas layanan melalui program merdeka belajar. Feniks Muda Sejahtera.
Tesio, L., Caronni, A., Simone, A., Kumbhare, D., & Scarano, S. (2023). Interpreting results from Rasch analysis 2 . Advanced model applications and the data-model fit assessment. Disability and Rehabilitation, 1–15. https://doi.org/10.1080/09638288.2023.2169772
Umerenkova, A. G., Alba, M., Cendales, B., Espinosa, J. C., & Ariza, J. (2025). Integrated validation of a cross-cultural emotional competence scale using Confirmatory Factor Analysis, Rasch Modeling and Exploratory Graph Analysis. MethodsX, 15, 1–10. https://doi.org/10.1016/j.mex.2025.103627
Valdes, M. G., Riquelme, A. G., & Casal, G. B. (2022). Content Validity through Expert Judgment for the Depression Clinical Evaluation Test. International Journal of Clinical and Health Psychology, 22, 1–8. https://doi.org/10.1016/j.ijchp.2022.100292
Van Zile-Tamsen, C. (2017). Using Rasch analysis to inform rating scale development. Research in Higher Education, 58(8), 922-933. https://doi.org/10.1007/s11162-017-9448-0
Viola, M. A., Vilanti, F. A., Rahman, I. A., Masita, M., & Setiyadi, B. (2024). Community-Based Curriculum Analysis: Utilizing Local Content Curriculum for Contextual Education. Journal of Education and Learning Dimensions, 12(1). https://doi.org/10.24269/dpp.v12i1.9636
Yanti, F. A., & Thohir, M. A. (2024). Higher order thinking skills in science learning : a systematic review from 2014-2023. International Journal of Evaluation and Research in Education (IJERE), 13(4), 2419–2427. https://doi.org/10.11591/ijere.v13i4.28082
Zaky, M., Astija, Supriyatman, Muslimin, Jamhari, M., & Ratman. (2024). STEM Literacy Proficiency in Prospective Physics Educator: A Comprehensive Analysis Using Rasch Measurement Theory. Jurnal Penelitian Pendidikan IPA, 10(12), 10799–10810. https://doi.org/10.29303/jppipa.v10i12.8970
Zebua, N. (2025). Education Transformation : Implementation of Deep Learning in 21st-Century Learning. Harmoni Pendidikan: Journal of Education Science, 2(2), 146–152. https://doi.org/10.62383/hardik.v2i2.1405
License
Copyright (c) 2026 Yerimadesi, Iryani, Faizah Qurrata Aini, Fauzana Gazali, Nofri Yuhelman, Rivo Juita Sudirman, Diva Salsabila, Zaqiya Zahwa Alifa, Mentari Az Zahra
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
