Building Scientific Reasoning Using a Data Literacy Module in Higher Education
DOI:
https://doi.org/10.58706/ijorce.v3n2.p110-124Keywords:
Scientific Reasoning, Data Literacy, E-Module, Learning Resource, Undergraduate EducationAbstract
Scientific reasoning is a fundamental skill that must be developed in higher education, particularly in science-related disciplines. However, existing learning resources often lack an explicit focus on data interpretation and evidence-based analysis, both essential for scientific reasoning. This study aimed to develop and evaluate the validity, practicality, and effectiveness of a data literacy-based e-module designed to enhance undergraduate students’ scientific reasoning skills. Employing a design and development approach based on the ADDIE model, the research included analysis, design, development, implementation, and evaluation stages. The e-module was developed by aligning curriculum needs, scientific reasoning indicators, and core data literacy principles. Content and construct validity were assessed through expert reviews, yielding high validity scores (above 3.7) and strong reliability (α = 0.83-0.94). Small-scale trials with undergraduate science education students demonstrated high levels of learning activity (72%-94%) and consistent student engagement throughout the learning phases. Students' scientific reasoning improved significantly, with N-Gain scores ranging from 0.49 to 0.74, categorized as moderate to high, particularly in indicators such as proportional thinking. Statistical tests indicated no significant differences between class groups, suggesting consistent effectiveness across cohorts. Student feedback was overwhelmingly positive, with agreement levels reaching 100% on most evaluation indicators. This research contributes to the advancement of pedagogical design by integrating data literacy into the scientific reasoning process, offering a replicable learning resources that can be adapted across science disciplines to better prepare students for data-driven scientific inquiry in the digital era.
References
Aditomo, A. & Klieme, E. (2020). Forms of inquiry-based science instruction and their relations with learning outcomes: evidence from high and low-performing education systems. International Journal of Science Education, 42(4), 504-525. DOI: https://doi.org/10.1080/09500693.2020.1716093.
Adri, H.T. & Suwarjono. (2023). Developing science e-modules based on scientific reasoning skills for primary education course. Jurnal Penelitian Pendidikan IPA, 9(8), 6480-6486. DOI: https://doi.org/10.29303/jppipa.v9i8.4727.
Ahmed, A.M. (2019). Students’ reflective journaling: An impactful strategy that informs instructional practices in an EFL writing university context in Qatar. Reflective Practice, 20(4), 483-500. DOI: https://doi.org/10.1080/14623943.2019.1638246.
Andani, S.R., Sari, S.Y., Akmam, A., & Sundari, P.D. (2023). The urgency of interactive learning media in improving students’ physics learning outcomes. Jurnal Ilmiah Profesi Pendidikan, 8(4), 2415-2419. DOI: https://doi.org/10.29303/jipp.v8i4.1814.
Andrienko, N., Lammarsch, T., Andrienko, G., Fuchs, G., Keim, D., Miksch, S., & Rind, A. (2018). Viewing visual analytics as model building. Computer Graphics Forum, 37(6), 275-299. DOI: https://doi.org/10.1111/cgf.13324.
Bolkan, S. & Goodboy, A.K. (2024). Conditional indirect effects of clarity on students’ information processing: disentangling sources of cognitive load. Communication Education, 73(3), 247-266. DOI: https://doi.org/10.1080/03634523.2024.2303438.
Chistyakov, A.A., Zhdanov, S.P., Avdeeva, E.L., Dyadichenko, E.A., Kunitsyna, M.L., & Yagudina, R.I. (2023). Exploring the characteristics and effectiveness of project-based learning for science and STEAM education. Eurasia Journal of Mathematics Science and Technology Education, 19(5), em2256. DOI: https://doi.org/10.29333/ejmste/13128.
Cook, E., Kennedy, E., & McGuire, S.Y. (2013). Effect of teaching metacognitive learning strategies on performance in general chemistry courses. Journal of Chemical Education, 90(8), 961-967. DOI: https://doi.org/10.1021/ed300686h.
Crujeiras-Pérez, B. & Jiménez-Aleixandre, M.P. (2019). Students’ progression in monitoring anomalous results obtained in inquiry-based laboratory tasks. Research in Science Education, 49(1), 243-264. DOI: https://doi.org/10.1007/s11165-017-9641-3.
Cui, Y., Chen, F., Lutsyk, A., Leighton, J.P., & Cutumisu, M. (2023). Data literacy assessments: A systematic literature review. Assessment in Education: Principles, Policy and Practice, 30(1), 76-96. DOI: https://doi.org/10.1080/0969594X.2023.2182737.
Dewi, C.T., Kuswanto, H., Rahmat, A.D., & Sihana. (2024). The effectiveness of virtual laboratory integrated blacksmith ethnophysics in enhancing data literacy and graphic representation skills. International Journal of Information and Education Technology, 14(8), 1155-1163. DOI: https://doi.org/10.18178/ijiet.2024.14.8.2144.
Edelsbrunner, P.A. & Dablander, F. (2019). The psychometric modeling of scientific reasoning: a review and recommendations for future avenues. Educational Psychology Review, 31, 1-34. DOI: https://doi.org/10.1007/s10648-018-9455-5.
Engelmann, K., Neuhaus, B.J., & Fischer, F. (2016). Fostering scientific reasoning in education-meta-analytic evidence from intervention studies. Educational Research and Evaluation, 22(5-6), 333-349. DOI: https://doi.org/10.1080/13803611.2016.1240089.
Erlina, N., Susantini, E., Wasis, W., Wicaksono, I., & Pandiangan, P. (2018). Evidence-based reasoning in inquiry-based physics teaching to increase students’ scientific. Journal of Baltic Science Education, 17(6), 972-985. DOI: https://doi.org/10.33225/jbse/18.17.972.
Eshach, H. & Kukliansky, I. (2016). Developing of an instrument for assessing students’ data analysis skills in the undergraduate physics laboratory. Canadian Journal of Physics, 94(11), 1205-1215. DOI: https://doi.org/10.1139/cjp-2016-0308.
Espinoza-Cedeño, L.E., Solorzano-Solorzano, A.A., & Castillo-Bravo, E.F. (2024). Digital teaching resources to enhance logical thinking. International Journal of Social Sciences, 7(4), 135-141. DOI: https://doi.org/10.21744/ijss.v7n4.2349.
Fielding, J., Makar, K., & Ben-Zvi, D. (2025). Developing students’ reasoning with data and data-ing. ZDM - Mathematics Education, 57(1), 1-18. DOI: https://doi.org/10.1007/s11858-025-01671-6.
Firat, E.E., Denisova, A., Wilson, M.L., & Laramee, R.S. (2022). P-Lite: A study of parallel coordinate plot literacy. Visual Informatics, 6(3), 81-99. DOI: https://doi.org/10.1016/j.visinf.2022.05.002.
Fragou, O. & Papadopoulou, M. (2020). Exploring infographic design in higher education context: Towards a modular evaluation framework. Journal of Visual Literacy, 39, 1-22. DOI: https://doi.org/10.1080/1051144X.2020.1737904.
Geel, M., Keuning, T., Visscher, A., & Fox, J.-P. (2017). Changes in educators’ data literacy during a data-based decision making intervention. Teaching and Teacher Education, 64, 187-198. DOI: https://doi.org/10.1016/j.tate.2017.02.015.
Glaze, A.L. (2018). Teaching and learning science in the 21st century: Challenging critical assumptions in post-secondary science. Education Sciences, 8(1), 12. DOI: https://doi.org/10.3390/educsci8010012.
Hake, R.R. (1999). Analyzing change/gain scores. Washington DC: American Educational Research Association.
Handayani, F., Fitria, Y., Ahmad, S., & Zen, Z. (2023). Development of E-module based on problem based learning assisted with scratch applications to improve students computational thinking skills. Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran Dan Pembelajaran, 9(2), 456. DOI: https://doi.org/10.33394/jk.v9i2.7790.
Higgins, V. & Carter, J. (2025). Thinking creatively about the data skills gap: how online training events are supporting shape higher education students. Journal of Statistics and Data Science Education, 9169(May), 1-12. DOI: https://doi.org/10.1080/26939169.2025.2483228.
Hrouzková, T. & Richterek, L. (2021). Lawson classroom test of scientific reasoning at entrance university level. Proceedings of the 4th International Baltic Symposium on Science and Education, 74-85. DOI: https://doi.org/10.33225/balticste/2021.74.
Jaya, F. & Nurqamarani, A.S. (2023). Understanding student engagement in hybrid learning: An analysis impact digital literacy and academic self-efficacy. Jurnal Kependidikan Jurnal Hasil Penelitian Dan Kajian Kepustakaan di Bidang Pendidikan Pengajaran dan Pembelajaran, 9(3), 833. DOI: https://doi.org/10.33394/jk.v9i3.7947.
Jiang, S., Tatar, C., Huang, X., Sung, S.H., & Xie, C. (2022). Augmented reality in science laboratories: Investigating high school students’ navigation patterns and their effects on learning performance. Journal of Educational Computing Research, 60(3), 777-803. DOI: https://doi.org/10.1177/07356331211038764.
Kanari, Z., & Millar, R. (2004). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science Teaching, 41(7), 748-769. DOI: https://doi.org/10.1002/tea.20020.
Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51-65. DOI: https://doi.org/10.1080/23735082.2015.1002195.
Kjelvik, M.K. & Schultheis, E.H. (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE, Life Sciences Education, 18(2), es2. DOI: https://doi.org/10.1187/cbe.18-02-0023.
Krell, M., Redman, C., Mathesius, S., Krüger, D., & Driel, J.V. (2020). Assessing pre-service science teachers’ scientific reasoning competencies. Research in Science Education, 50, 2305-2329. DOI: https://doi.org/10.1007/s11165-018-9780-1.
Kumi-Yeboah, A., Sallar, A., Kiramba, L.K., & Kim, Y. (2020). Exploring the use of digital technologies from the perspective of diverse learners in online learning environments. Online Learning, 24(4), 42-63. DOI: https://doi.org/10.24059/olj.v24i4.2323.
Lastariwati, B., Komariah, K., Mulyatiningsih, E., & Kartika, M.G. (2021). Exploration of the determining factors of successful online learning in the industrial revolution 4.0 era. Journal of Physics: Conference Series, 1833(1), 012069. DOI: https://doi.org/10.1088/1742-6596/1833/1/012069.
Lazonder, A.W., Janssen, N., Gijlers, H., & Walraven, A. (2021). Patterns of development in children’s scientific reasoning: Results from a three-year longitudinal study. Journal of Cognition and Development, 22(1), 108-124. DOI: https://doi.org/10.1080/15248372.2020.1814293.
Liao, X., Zhang, X., Wang, Z., & Luo, H. (2024). Design and implementation of an AI-enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning: An experimental study. British Journal of Educational Technology, 55(3), 1253-1276. DOI: https://doi.org/10.1111/bjet.13424.
Lin, C.-J., Lee, H.-Y., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024). Enhancing STEM collaboration through reflective strategies in the 6E learning model: Insights from voice recognition analysis. Education and Information Technologies, 30(4), 4251-4276. DOI: https://doi.org/10.1007/s10639-024-12957-9.
Lin, R., Yang, J., Jiang, F., & Li, J. (2023). Does teacher’s data literacy and digital teaching competence influence empowering students in the classroom? Evidence from China. Education and Information Technologies, 28(3), 2845-2867. DOI: https://doi.org/10.1007/s10639-022-11274-3.
Marušić, M. & Dragojević, A. (2020). Assessing pharmacy students’ scientific reasoning after completing a physics course taught using active-learning methods. American Journal of Pharmaceutical Education, 84(8), 1112-1122. DOI: https://doi.org/10.5688/ajpe7610.
Masnick, A.M. & Morris, B.J. (2022). A model of scientific data reasoning. Education Sciences, 12(2), 71. DOI: https://doi.org/10.3390/educsci12020071.
Mayer, R.E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36(1), 8. DOI: https://doi.org/10.1007/s10648-023-09842-1.
McGowan, B.S., Ekeigwe, A., & Clase, K. (2022). Designing and assessing a data literacy internship program for graduate health sciences students. Journal of the Medical Library Association, 110(4), 501-506. DOI: https://doi.org/10.5195/jmla.2022.1498.
Meister, S., Krell, M., Göhner, M., & Upmeier zu Belzen, A. (2020). Pre-service biology teachers’ responses to first-hand anomalous data during modelling processes. Research in Science Education, 51(6), 1459-1479. DOI: https://doi.org/10.1007/s11165-020-09929-7.
Nasip, A.M. & Ong, E.T. (2021). 5E inquiry-based teaching module for primary school science teachers: A need analysis. Review of International Geographical Education Online, 11(4), 730-737. DOI: https://doi.org/10.33403/rigeo.8006786.
Ningrum, T.W., Handayani, R. D., & Maryani, M. (2024). Investigasi kemampuan bernalar ilmiah siswa melalui implementasi model problem based learning materi fisika fluida statis. Jurnal Pendidikan Fisika, 12(1), 68. DOI: https://doi.org/10.24127/jpf.v12i1.9433.
Ningsih, W., Prayitno, B.A., & Santosa, S. (2023). The effectiveness of environmentoriented e-books based on problem-based learning for problem-solving skills. JPBI (Jurnal Pendidikan Biologi Indonesia), 9(3), 511-520. DOI: https://doi.org/10.22219/jpbi.v9i3.25603.
Novia, N. & Riandi, R. (2017). The analysis of students scientific reasoning ability in solving the modified lawson classroom test of scientific reasoning (MLCTSR) problems by applying the levels of inquiry. Jurnal Pendidikan IPA Indonesia, 6(1), 116-122. DOI: https://doi.org/10.15294/jpii.v6i1.9600.
Nurissamawati, B., Yuniawatika, & Mas’ula, S. (2015). E-module development using problem-based learning approach to learn data processing in training students’ critical thinking. JINoP (Jurnal Inovasi Pembelajaran), 1(1), 62-79. DOI: https://doi.org/10.22219/jinop.v1i1.2441.
Nurussalamah, U., Fatmaryanti, S.D., & Sriyono, N. (2025). Integrating e-module with Project Based Learning (PjBL): A strategy to improve students’ collaboration abilities and scientific literacy. Jurnal Penelitian Pendidikan IPA, 11(3), 936-945. DOI: https://doi.org/10.29303/jppipa.v11i3.10864.
O’Connor, M.J., Hardi, J., Martínez-Romero, M., Somasundaram, S., Honick, B., Fisher, S.A., Pillai, A., & Musen, M.A. (2025). Ensuring adherence to standards in Experiment-Related metadata entered via spreadsheets. Scientific Data, 12(1), 265. DOI: https://doi.org/10.1038/s41597-025-04589-6.
Ong, E.T., Govindasamy, D., Singh, C.K.S., Ibrahim, M.N., Wahab, N.A., Borhan, M.T., & Tho, S.W. (2021). The 5E inquiry learning model: Its effect on the learning of electricity among malaysian students. Cakrawala Pendidikan, 40(1), 170-182. DOI: https://doi.org/10.21831/cp.v40i1.33415.
Ow-Yeong, Y.K., Yeter, I.H., & Ali, F. (2023). Learning data science in elementary school mathematics: a comparative curriculum analysis. International Journal of STEM Education, 10(1), 8. DOI: https://doi.org/10.1186/s40594-023-00397-9.
Özdeniz, Y., Aktamış, H., & Bildiren, A. (2023). The effect of differentiated science module application on thescientific reasoning and scientific process skills of giftedstudents in a blended learning environment. International Journal of Science Education, 45(10), 827-849. DOI: https://doi.org/10.1080/09500693.2023.2175627.
Özel, A. (2023). Developing a media literacy perception scale. Research in Educational Policy and Management, 5(2), 144-162. DOI: https://doi.org/10.46303/repam.2023.14.
Panggalih, R.H. & Handayani, D.E. (2023). Pengembangan media pembelajaran materi sistem pernapasan manusia berbantukan aplikasi sac untuk sekolah dasar. Jurnal Tarbiyah, 30(1), 176-190. DOI: http://dx.doi.org/10.30829/tar.v30i1.2693.
Pereira, E., Nsair, S., Pereira, L.R., & Grant, K. (2024b). Constructive alignment in a graduate-level project management course: An innovative framework using large language models. International Journal of Educational Technology in Higher Education, 21(1), 25. DOI: https://doi.org/10.1186/s41239-024-00457-2.
Potvin, P. (2021). Response of science learners to contradicting information: a review of research. Studies in Science Education, 59(1), 67-108. DOI: https://doi.org/10.1080/03057267.2021.2004006.
Puspitasari, R.D., Herlina, K., & Suyatna, A. (2020). A need analysis of STEM-integrated flipped classroom e-module to improve critical thinking skills. Indonesian Journal of Science and Mathematics Education, 3(2), 178-184. DOI: https://doi.org/10.24042/ijsme.v3i2.6121.
Rahayu, I. & Sukardi, S. (2021). The development of E-Modules project based learning for students of computer and basic networks at vocational school. Journal of Education Technology, 4(4), 398. DOI: https://doi.org/10.23887/jet.v4i4.29230.
Sander, I. (2020). What is critical big data literacy and how can it be implemented? Internet Policy Review, 9(2), 1-22. DOI: https://doi.org/10.14763/2020.2.1479.
Saraç, H. (2018). The effect of science, technology, engineering and mathematics-stem educational practices on students’ learning outcomes: A meta-analysis study. Turkish Online Journal of Educational Technology - TOJET, 17(2), 125-142. Retrieved from: https://eric.ed.gov/?id=EJ1176176.
Schreiter, S., Friedrich, A., Fuhr, H., Malone, S., Brünken, R., Kuhn, J., & Vogel, M. (2024). Teaching for statistical and data literacy in K-12 STEM education: a systematic review on teacher variables, teacher education, and impacts on classroom practice. ZDM - Mathematics Education, 56(1), 31-45. DOI: https://doi.org/10.1007/s11858-023-01531-1.
Setiawan, B., Innatesari, D.K., & Sabtiawan, W.B. (2017). The development of local wisdom-based natural science module to improve science literation of students. Jurnal Pendidikan IPA Indonesia, 6(1), 49-54. DOI: https://doi.org/10.15294/jpii.v6i1.9595.
Shofiyah, N., Jatmiko, B., Suprapto, N., Prahani, B.K., & Anggraeni, D.M. (2025). The use of technology to scientific reasoning in science education: A bibliometric and content analysis of research papers. Social Sciences and Humanities Open, 11, 101534. DOI: https://doi.org/10.1016/j.ssaho.2025.101534.
Sinha, T. & Kapur, M. (2021). When problem solving followed by instruction works: Evidence for productive failure. Review of Educational Research, 91(5), 761-798. DOI: https://doi.org/10.3102/00346543211019105.
Siwawetkul, W. & Koraneekij, P. (2020). Effect of 5e instructional model on mobile technology to enhance reasoning ability of lower primary school students. Kasetsart Journal of Social Sciences, 41(1), 40-45. DOI: https://doi.org/10.1016/j.kjss.2018.02.005.
Speirs, J.C., Stetzer, M.R., Lindsey, B.A., & Kryjevskaia, M. (2021). Exploring and supporting student reasoning in physics by leveraging dual-process theories of reasoning and decision making. Physical Review Physics Education Research, 17(2), 20137. DOI: https://doi.org/10.1103/PhysRevPhysEducRes.17.020137.
Suárez, Á., Specht, M., Prinsen, F., Kalz, M., & Ternier, S. (2018). A review of the types of mobile activities in mobile inquiry-based learning. Computers and Education, 118(March 2017), 38-55. DOI: https://doi.org/10.1016/j.compedu.2017.11.004.
Tian, P., Sun, D., Han, R., & Fan, Y. (2023). Integrating micro projectbased learning to improve conceptual understanding and crucial learning skills in chemistry. Journal of Baltic Science Education, 22(1), 130-152. DOI: https://doi.org/10.33225/jbse/23.22.130.
Turrin, M. (2015). A day in the field: A stepped model to developing data-savvy students. The Science Teacher, 82(5), 35-42. DOI: https://doi.org/10.2505/4/tst15_082_05_35.
Vahey, P., Rafanan, K., Patton, C., Swan, K., van’t Hooft, M., Kratcoski, A., & Stanford, T. (2012). A cross-disciplinary approach to teaching data literacy and proportionality. Educational Studies in Mathematics, 81(2), 179-205. DOI: https://doi.org/10.1007/s10649-012-9392-z.
Vance, E.A., Glimp, D.R., Pieplow, N.D., Garrity, J.M., & Melbourne, B.A. (2022). Integrating the humanities into data science education: Reimagining the introductory data science course. Statistics Education Research Journal, 21(2), 9. DOI: https://doi.org/10.52041/serj.v21i2.42.
Vermeire, L., Van den Broeck, W., Petersen, F., & Van Audenhove, L. (2025). Beyond numeracy, a data literacy topical scoping review (2011-2023). Media and Communication, 13, 1-24. DOI: https://doi.org/10.17645/mac.9237.
Waruwu, M., Dwikurnaningsih, Y., & Satyawati, S.T. (2023). Online-based activities to improve students’ critical thinking, problem solving, and communication. International Journal of Evaluation and Research in Education, 12(3), 1645-1653. DOI: https://doi.org/10.11591/ijere.v12i3.24719.
Whelan, K. & Paez, A. (2019). Development of a new framework to guide, assess, and evaluate student reflections in a university sustainability course. Teaching & Learning Inquiry the ISSOTL Journal, 7(1), 55-77. DOI: https://doi.org/10.20343/teachlearninqu.7.1.5.
Williams, A.T., & Svensson, M. (2020). Student teachers’ collaborative learning of science in small-group discussions. Scandinavian Journal of Educational Research, 65(6), 914-927. DOI: https://doi.org/10.1080/00313831.2020.1788141.
Wolff, A., Wermelinger, M., & Petre, M. (2019). Exploring design principles for data literacy activities to support children’s inquiries from complex data. International Journal of Human-Computer Studies, 129, 41-54. DOI: https://doi.org/10.1016/j.ijhcs.2019.03.006.
Wright, C., Meng, Q., Breshock, M.R., Atta, L., Taub, M.A., Jager, L.R., Muschelli, J., & Hicks, S.C. (2024). Open case studies: Statistics and data science education through real-world applications. Journal of Statistics and Data Science Education, 32(4), 331-344. DOI: https://doi.org/10.1080/26939169.2024.2394541.
Yasa, A.D., Rahayu, S., Handayanto, S.K., & Ekawati, R. (2024). Evaluating the impact of smart learning-based inquiry on enhancing digital literacy and critical thinking skills. Ingénierie des Systèmes d’Information, 29(1), 219-233. DOI: https://doi.org/10.18280/isi.290122.
Zeitlhofer, I., Zumbach, J., & Schweppe, J. (2024). Complexity affects performance, cognitive load, and awareness. Learning and Instruction, 94(October 2023), 102001. DOI: https://doi.org/10.1016/j.learninstruc.2024.102001.
Zimmerman, C., Olsho, A., Brahmia, S. W., Loverude, M., Boudreaux, A., & Smith, T. (2019). Towards understanding and characterizing expert covariational reasoning in physics. Physics Education Research Conference Proceedings, 693-698. DOI: https://doi.org/10.1119/perc.2019.pr.Zimmerman.
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