Designing Blended Problem-Based Science Learning to Strengthen Scientific Literacy: A Contextual Study at SMP Al Wathan Ambon
Abstract
Scientific literacy requires lower-secondary students to explain phenomena, evaluate investigations, interpret data, and make evidence-informed decisions. This contextual design study develops an offline-first blended problem-based learning framework for Grade 9 science at SMP Al Wathan Ambon, Indonesia. The design responds to a 2022 source study that reported a statistically significant model effect but also presented modest learning gains and internally inconsistent effectiveness claims. Twenty-five sources published from 2021 to 2025 were synthesised across scientific-literacy assessment, problem-based and collaborative inquiry, blended-learning engagement, inclusive science education, curriculum alignment, and implementation feasibility. The resulting EVIDENCE-PBL cycle comprises Engage with a local phenomenon, Verify prior ideas and evidence needs, Investigate through coordinated offline and online resources, Discuss competing explanations, Evaluate data and source quality, Narrate an evidence-based claim, Check individual understanding, and Extend learning to a reasoned decision. Design outputs include a readiness audit, a low-bandwidth modality matrix, an 80-minute coastal-science lesson sequence, a four-domain literacy rubric, fidelity indicators, and a cautious pilot-evaluation protocol. Digital activities are treated as optional pedagogical resources rather than compulsory access points; equivalent printed and device-shared pathways are specified. Because no primary data were collected at SMP Al Wathan Ambon, the article reports a contextual intervention design rather than effectiveness findings. School identity, class profile, connectivity, safeguarding, and baseline literacy must be verified before field implementation.
References
[1] OECD, PISA 2022 Results (Volume I): The State of Learning and Equity in Education. Paris, France: OECD Publishing, 2023, doi: 10.1787/53f23881-en.
[2] OECD, PISA 2022 Assessment and Analytical Framework. Paris, France: OECD Publishing, 2023, doi: 10.1787/dfe0bf9c-en.
[3] OECD, PISA 2022 Results (Volume II): Learning During—and From—Disruption. Paris, France: OECD Publishing, 2023, doi: 10.1787/a97db61c-en.
[4] TIMSS & PIRLS International Study Center, TIMSS 2023 International Results in Mathematics and Science. Chestnut Hill, MA, USA: Boston College, 2024.
[5] A. A. Tawfik, J. J. Gishbaugher, J. Gatewood, and T. L. Arrington, “How K–12 teachers adapt problem-based learning over time,” Interdisciplinary Journal of Problem-Based Learning, vol. 15, no. 1, 2021.
[6] Global Education Monitoring Report Team, Global Education Monitoring Report 2023: Technology in Education—A Tool on Whose Terms? Paris, France: UNESCO, 2023, doi: 10.54676/UZQV8501.
[7] World Bank, UNESCO, UNICEF, FCDO, USAID, and Bill & Melinda Gates Foundation, The State of Global Learning Poverty: 2022 Update. Washington, DC, USA: World Bank, 2022.
[8] UNESCO and International Task Force on Teachers for Education 2030, Global Report on Teachers: Addressing Teacher Shortages and Transforming the Profession. Paris, France: UNESCO, 2024.
[9] M. A. Pratama and R. Zilhakim, “Pengaruh model pembelajaran Problem Based Learning berbasis Blended Learning terhadap literasi sains siswa di MTsN 1 Bengkulu Selatan,” Jurnal Jendela Pendidikan, vol. 2, no. 1, pp. 54–60, 2022.
[10] S. Lenzer, L. Pannullo, A. Nehring, and L. Stinken-Roesner, “Towards scientific literacy in inclusive science education,” arXiv:2411.03020, 2024.
[11] B. Setiaji, P. H. Santoso, K. N. Aziz, H. Retnawati, and M. Khairudin, “Using multilevel modeling to evaluate science literacy and technology course of Indonesian non-science students,” arXiv:2304.06782, 2023.
[12] Ministry of Primary and Secondary Education of the Republic of Indonesia, Regulation of the Minister of Primary and Secondary Education Number 13 of 2025 concerning the Amendment to Regulation Number 12 of 2024 on the Curriculum for Early Childhood, Primary, and Secondary Education. Jakarta, Indonesia, 2025.
[13] A. Saleh, T. M. Phillips, C. E. Hmelo-Silver, K. D. Glazewski, B. W. Mott, and J. C. Lester, “A learning analytics approach towards understanding collaborative inquiry in a problem-based learning environment,” British Journal of Educational Technology, vol. 53, no. 5, pp. 1321–1342, 2022, doi: 10.1111/bjet.13198.
[14] K. Nichols, R. Musofer, L. Fynes-Clinton, and R. Blundell, “Design thinking and inquiry behaviours are co-constituted in a community of inquiry middle years’ science classroom context,” International Journal of Technology and Design Education, 2022.
[15] T. K. Rogat, C. E. Hmelo-Silver, B. Cheng, A. Traynor, T. Adeoye, A. Gomoll, and B. Downing, “A multidimensional framework of groups’ productive disciplinary engagement,” Frontline Learning Research, vol. 10, no. 2, pp. 1–21, 2022.
[16] C. Eberbach, C. E. Hmelo-Silver, R. Jordan, J. Taylor, and R. Hunter, “Multidimensional trajectories for understanding ecosystems,” Science Education, vol. 105, no. 3, pp. 521–540, 2021, doi: 10.1002/sce.21613.
[17] A. Gomoll, C. E. Hmelo-Silver, and S. Šabanović, “Co-constructing professional vision: Teacher and researcher learning in co-design,” Cognition and Instruction, vol. 40, no. 1, pp. 7–26, 2022, doi: 10.1080/07370008.2021.2010210.
[18] C. Müller and T. Mildenberger, “Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education,” Educational Research Review, vol. 34, art. 100394, 2021, doi: 10.1016/j.edurev.2021.100394.
[19] G. Heilporn, S. Lakhal, and M. Bélisle, “An examination of teachers’ strategies to foster student engagement in blended learning in higher education,” International Journal of Educational Technology in Higher Education, vol. 18, art. 25, 2021, doi: 10.1186/s41239-021-00260-3.
[20] M. Bond, S. Bedenlier, V. I. Marín, and M. Händel, “Emergency remote teaching in higher education: Mapping the first global online semester,” International Journal of Educational Technology in Higher Education, vol. 18, art. 50, 2021, doi: 10.1186/s41239-021-00282-x.
[21] N. Carrete-Marín, L. Domingo-Peñafiel, and N. Simó-Gil, “Teaching materials for rural schools: Challenges and practical considerations from an international perspective,” International Journal of Educational Research Open, vol. 7, art. 100365, 2024, doi: 10.1016/j.ijedro.2024.100365.
[22] V. Braun and V. Clarke, “One size fits all? What counts as quality practice in (reflexive) thematic analysis?” Qualitative Research in Psychology, vol. 18, no. 3, pp. 328–352, 2021, doi: 10.1080/14780887.2020.1769238.
[23] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, art. n71, 2021, doi: 10.1136/bmj.n71.
[24] L. Chourio-Acevedo, J. Köhler, C. Coscarelli, D. Gacitúa, V. Proaño-Ríos, and R. González-Ibáñez, “Information literacy development and assessment at school level: A systematic review of the literature,” arXiv:2404.19020, 2024.
[25] W. F. S. Costa, J. A. Martins Júnior, A. F. G. Silva, R. X. Silva, and A. N. A. A. Silva, “Temperature mapping in urban biomes using an infrared thermometer: An investigative approach to physics education,” arXiv:2504.21088, 2025.
Sahal, M. S. (2026). Development of a Robust Shrinkage Empirical-Bayes P-Chart for Heterogeneous Proportion Data. Stationer: Journal of Statistical Innovations and Applications, 1(1), 57-66.
Panjaitan, A. Z., Tarigan, E. D. S. K. B., Ampun, H. U. A., & Dingga, P. M. (2026). A Comparison of Linear, Polynomial, and RBF Kernels in the Support Vector Machine Method for Diabetes Classification. Stationer: Journal of Statistical Innovations and Applications, 1(1), 01-12.
