Eurasia Journal of Mathematics, Science and Technology Education

• 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. https://doi.org/10.1080/09500693.2020.1716093
• Adúriz-Bravo, A. (2013). A ‘semantic’ view of scientific models for science education. Science & Education, 22, 1593–1611. https://doi.org/10.1007/s11191-011-9431-7
• Adúriz-Bravo, A. (2020). Contributions to the nature of science: Scientific investigation as inquiry, modeling, and argumentation. In C. N. El-Hani, M. Pietrocola, E. F. Mortimer, & M. R. Otero (Eds.), Science education research in Latin America (pp. 394–425). Brill/Sense. https://doi.org/10.1163/9789004409088_017
• Amador-Rodríguez, R., Insuasty, D., Méndez-López, M., & Márquez, E. (2021). Promover modelos explicativos sobre las interacciones químicas del felodipino-citocromo P450: Una propuesta didáctica basada en la modelización [Promoting explanatory models on the chemical interactions of felodipine-cytochrome P450: A didactic proposal based on modeling]. Química Nova, 44(10), 1379–1387. https://doi.org/10.21577/0100-4042.20170784
• Amador-Rodríguez, R., Insuasty, D., Méndez-López, M., Márquez, E., & Ospina-Quintero, N. (2023a). Explanatory models derived from context-based teaching: An investigation in undergraduate chemistry education. Journal of Chemical Education, 100(5), 1777-1787. https://doi.org/10.1021/acs.jchemed.2c01212
• Amador-Rodríguez, R., Valencia Cobo, J. A., & Lozano, E. E. (2023b). Visiones sobre la naturaleza de la ciencia en docentes: Pistas para pensar cambios en su formación [Teachers’ views on the nature of science: Clues for thinking about changes in their training]. Revista Eureka sobre Enseñanza y Divulgación de las Ciencias, 20(1), Article 1601. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2023.v20.i1.1601
• Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smit, J. H., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Educational Technology & Society, 19(3), 47-57.
• Badillo, R. G., Miranda, R. P., de Gallego, L. N. T., & Amador-Rodríguez, R. Y. (2024). Pre-service science teacher education in Colombia. In M. R. Quintanilla Gatica & A. Adúriz-Bravo (Eds.), Science teaching and a new teacher culture (Vol. 31, pp. 151-170). Springer. https://doi.org/10.1007/978-3-031-50004-6_9
• Bandura, A. (1990). Perceived self-efficacy in the exercise of personal agency. Journal of Applied Sport Psychology, 2(2), 128-163. https://doi.org/10.1080/10413209008406426
• Bandura, A. (2006). Guide for creating self-efficacy scales. In F. Pajares, & T. C. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307-338). Information Age Publishing.
• Barkley, E. F., Major, C. H., & Cross, K. P. (2014). Collaborative learning techniques: A handbook for college faculty. Jossey-Bass.
• Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning and Leading With Technology, 38, 20-23.
• Barrow, L. H. (2006). A brief history of inquiry: From Dewey to standards. Journal of Science Teacher Education, 17(3), 265-278. https://doi.org/10.1007/s10972-006-9008-5
• Bell, T., & Vahrenhold, J. (2018). CS unplugged—How is it used, and does it work? In H.-J. Böckenhauer, D. Komm, & W. Unger (Eds.), Adventures between lower bounds and higher altitudes: Essays dedicated to Juraj Hromkovič on the occasion of his 60^th birthday (pp. 497-521). Springer. https://doi.org/10.1007/978-3-319-98355-4_29
• Belmar, H. (2022). Review on the teaching of programming and computational thinking in the world. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.997222
• Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94(5), 765-793. https://doi.org/10.1002/sce.20402
• Bers, M. U. (2020). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge. https://doi.org/10.4324/9781003022602
• Boulden, D. C., Rachmatullah, A., Oliver, K. M., & Wiebe, E. (2021). Measuring in-service teacher self-efficacy for teaching computational thinking: Development and validation of the T-STEM CT. Education and Information Technologies, 26(4), 4663-4689. https://doi.org/10.1007/s10639-021-10487-2
• Brown, P. L., Abell, S. K., Demir, A., & Schmidt, F. J. (2006). College science teachers’ views of classroom inquiry. Science Education, 90(5), 784–802. https://doi.org/10.1002/sce.20151
• Bybee, R. (2011). Scientific and engineering practices in K-12 classrooms. Science Teacher, 78(9), 34-40.
• Cairns, D. (2019). Investigating the relationship between instructional practices and science achievement in an inquiry-based learning environment. International Journal of Science Education, 41(15), 2113-2135. https://doi.org/10.1080/09500693.2019.1660927
• Cakir, M. (2011). L. B. Flick and N. G. Lederman (eds): Scientific inquiry and nature of science: Implications for teaching, learning, and teacher education. Science & Education, 20(3-4), 381-387. https://doi.org/10.1007/s11191-010-9274-7
• Castillo-Hernández, F.-J., Jiménez-Liso, M.-R., & Couso, D. (2022). Can we do real inquiry online? Influence of real-time data collection on students’ views of inquiry in an online, multi-site masters’ degree on environmental education. Journal of Computing in Higher Education, 34(3), 608-632. https://doi.org/10.1007/s12528-022-09312-7
• Chaia, A., Child, F., Dorn, E., Frank, M., & Krawitz, M. (2017). Drivers of student performance: Latin America insights. McKinsey&Company.
• Couso, D. (2014). Lagarón, D. C. (2015). De la moda de “aprender indagando” a la indagación para modelizar: Una reflexión crítica [From the trend of “learning by inquiring” to inquiry for modeling: A critical reflection]. In Proceedings of the 26^th International Symposium on Didactics of Social Sciences.
• Dabholkar, S., Arastoopour Irgens, G., Horn, M., & Wilensky, U. (2020). Students’ epistemic connections between science inquiry practices and disciplinary ideas in a computational science unit. In Proceedings of the International Conference of the Learning Sciences.
• Deci, E., & Ryan, R. (2004). Handbook of self-determination research. University Rochester Press.
• Develaki, M. (2017). Using computer simulations for promoting model-based reasoning: Epistemological and educational dimensions. Science & Education, 26(7-9), 1001-1027. https://doi.org/10.1007/s11191-017-9944-9
• Diordieva, C., Yeter, I. H., & Smith, W. (2019). Middle school STEM teachers’ understandings of computational thinking: A case study of Brazil and the USA. In Proceeding of the American Society for Engineering Education Conference & Exposition. https://doi.org/10.18260/1-2--33107
• Espinal, A., Vieira, C., & Guerrero-Bequis, V. (2023). Student ability and difficulties with transfer from a block-based programming language into other programming languages: A case study in Colombia. Computer Science Education, 33(4), 567–599. https://doi.org/10.1080/08993408.2022.2079867
• Espinal, A., Vieira, C., & Magana, A. J. (2024). Professional development in computational thinking: A systematic literature review. ACM Transactions on Computing Education, 24(2), Article 27. https://doi.org/10.1145/3648477
• Farris, A. V., Dickes, A. C., & Sengupta, P. (2016). Development of disciplined interpretation using computational modeling in the elementary science classroom. In Proceedings of the 12^th International Conference of the Learning Sciences (pp. 282-289).
• Feyerabend, P. (1975). ‘Science.’ The myth and its role in society. Inquiry, 18(2), 167-181. https://doi.org/10.1080/00201747508601758
• Furber, S. (2012). Shut down or restart? The way forward for computing in UK schools. The Royal Society. https://royalsociety.org/-/media/education/computing-in-schools/2012-01-12-computing-in-schools.pdf
• Gillies, R. M., & Baffour, B. (2017). The effects of teacher-introduced multimodal representations and discourse on students’ task engagement and scientific language during cooperative, inquiry-based science. Instructional Science, 45(4), 493-513. https://doi.org/10.1007/s11251-017-9414-4
• Gómez, R. L., & Suárez, A. M. (2020). Do inquiry-based teaching and school climate influence science achievement and critical thinking? Evidence from PISA 2015. International Journal of STEM Education, 7(1), Article 43. https://doi.org/10.1186/s40594-020-00240-5
• Hodson, D. (2003). Time for action: Science education for an alternative future. International Journal of Science Education, 25(6), 645-670. https://doi.org/10.1080/09500690305021
• Izquierdo-Aymerich, M., & Adúriz-Bravo, A. (2003). Epistemological foundations of school science. Science & Education, 12(1), 27–43. https://doi.org/10.1023/A:1022698205904
• Izquierdo Aymerich, M., & Adúriz-Bravo, A. (2021). Contribuciones de Giere a la reflexión sobre la educación científica [Giere's contributions to the reflection on science education]. ArtefaCToS. Revista de Estudios Filosóficos sobre Ciencia y Tecnología, 10(1), 75–87. https://doi.org/10.14201/art20211017587
• Jiménez Aleixandre, M. P. (2012). Las prácticas científicas en la investigación y en la clase de ciencias [Scientific practices in research and in the science classroom]. In Proceedings of the 25^th Meetings on the Didactics of Experimental Sciences (pp. 9-15). APICE/University of Santiago de Compostela.
• Justi, R., & Gilbert, J. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25(11), 1369-1386. https://doi.org/10.1080/0950069032000070324
• Katai, Z. (2020). Promoting computational thinking of both sciences- and humanities-oriented students: An instructional and motivational design perspective. Educational Technology Research and Development, 68(5), 2239-2261. https://doi.org/10.1007/s11423-020-09766-5
• Kelly, G. J. (2008). Inquiry, activity and epistemic practice. In J. S. Lederman (Ed.), Teaching scientific inquiry (pp. 99-117). Cengage. https://doi.org/10.1163/9789460911453_009
• Kelly, G. J. (2014). Inquiry teaching and learning: Philosophical considerations. In M. Matthews (Eds.), International handbook of research in history, philosophy and science teaching (pp. 1363-1380). Springer. https://doi.org/10.1007/978-94-007-7654-8_42
• Ketelhut, D. J. (2007). The impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in River City, a multi-user virtual environment. Journal of Science Education and Technology, 16(1), 99-111. https://doi.org/10.1007/s10956-006-9038-y
• Kite, V., & Park, S. (2023). What’s computational thinking?: Secondary science teachers’ conceptualizations of computational thinking (CT) and perceived barriers to CT integration. Journal of Science Teacher Education, 34(4), 391-414. https://doi.org/10.1080/1046560X.2022.2110068
• Klemencic, M. (2015). ‘What is student agency? An ontological exploration in the context of research on student engagement.’ In M. Klemencic, S. Bergan, & R. Primozic (Eds.), Student engagement in Europe: Society, higher education and student governance (pp. 11-29). Council of Europe.
• Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., Malyn-Smith, J., & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32-37. https://doi.org/10.1145/1929887.1929902
• Liang, L. L., Chen, S., Chen, X., Kaya, O. N., Adams, A. D., Macklin, M., & Ebenezer, J. (2009). Preservice teachers’ views about nature of scientific knowledge development: An international collaborative study. International Journal of Science and Mathematics Education, 7(5), 987-1012. https://doi.org/10.1007/s10763-008-9140-0
• Mouza, C., Yang, H., Pan, Y.-C., Yilmaz Ozden, S., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 61-76. https://doi.org/10.14742/ajet.3521
• National Research Council. (1996). National science education standards. The National Academies Press.
• National Research Council. (2000). Inquiry and the national science education standards. The National Academies Press.
• National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press.
• OECD. (2019). PISA 2018 assessment and analytical framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en
• Ogegbo, A. A., & Ramnarain, U. (2022). Teachers’ perceptions of and concerns about integrating computational thinking into science teaching after a professional development activity. African Journal of Research in Mathematics, Science and Technology Education, 26(3), 181-191. https://doi.org/10.1080/18117295.2022.2133739
• Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: An overview. International Journal of Science Education, 33(8), 1109-1130. https://doi.org/10.1080/09500693.2010.502191
• Passmore, C., Gouvea, J. S., & Giere, R. (2014). Models in science and in learning science: Focusing scientific practice on sense-making. In M. R. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 1171-1202). Springer. https://doi.org/10.1007/978-94-007-7654-8_36
• Psycharis, S., Kalovrektis, K., Xenakis, A., Paliokas, I., Patrinopoulos, M., Georgiakakis, P., Iatrou, P., Theodorou, P., Papageorgiou, T., & Ntourou, V. (2021). The impact of physical computing and computational pedagogy on girl’s self-efficacy and computational thinking practice. In 2021 IEEE Global Engineering Education Conference (pp. 308-315). IEEE. https://doi.org/10.1109/EDUCON46332.2021.9454003
• Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88(3), 345-372. https://doi.org/10.1002/sce.10130
• Sengupta, P., Dickes, A., & Farris, A. (2018). Toward a phenomenology of computational thinking in STEM education. In M. Khine (Eds.), Computational thinking in the STEM disciplines (pp. 49-72). Springer. https://doi.org/10.1007/978-3-319-93566-9_4
• Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380. https://doi.org/10.1007/s10639-012-9240-x
• Sherwood, H., Culp, K. M., Ferguson, C., Kaiser, A., Henry, M., & Negron, A. (2024). Teacher practices for formatively assessing computational thinking with early elementary learners. Education Sciences, 14(11), Article 1250. https://doi.org/10.3390/educsci14111250
• Sulistiyo, M. A. S., & Wijaya, A. (2020). The effectiveness of inquiry-based learning on computational thinking skills and self-efficacy of high school students. Journal of Physics: Conference Series, 1581, Article 012046. https://doi.org/10.1088/1742-6596/1581/1/012046
• Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
• Tsakeni, M. (2021). Preservice teachers’ use of computational thinking to facilitate inquiry-based practical work in multiple-deprived classrooms. Eurasia Journal of Mathematics, Science and Technology Education, 17(1), Article em1933. https://doi.org/10.29333/ejmste/9574
• Vieira, C., Espinal, A., Chiu, J., Bredder, E., Harris, P. & Wilkens, K. (2024) Hacia una educación equitativa en ciencias de la computación [Towards equitable education in computer science]. Revista EIA, 21(42), Article 4210. https://doi.org/10.24050/reia.v21i42.1734
• Vieira, C., Gómez, R. L., Gómez, M., Canu, M., & Duque, M. (2023). Implementing unplugged CS and use-modify-create to develop student computational thinking skills: A nationwide implementation in Colombia. Educational Technology & Society, 26(3), 155-175. https://doi.org/10.30191/ETS.202307_26(3).0012
• Vieira, C., Magana, A. J., & Boutin, M. (2018a). Add‐on preferential groups (APG): Analyzing student preferences of teaching methods. Computer Applications in Engineering Education, 26(4), 1020–1032. https://doi.org/10.1002/cae.21953
• Vieira, C., Magana, A. J., García, R. E., Jana, A., & Krafcik, M. (2018b). Integrating computational science tools into a thermodynamics course. Journal of Science Education and Technology, 27(4), 322-333. https://doi.org/10.1007/s10956-017-9726-9
• Vieira, C., Magana, A. J., Roy, A., & Falk, M. L. (2019). Student explanations in the context of computational science and engineering education. Cognition and Instruction, 37(2), 201–231. https://doi.org/10.1080/07370008.2018.1539738
• Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5
• Wilkerson, M. H., Shareff, R., Laina, V., & Gravel, B. (2018). Epistemic gameplay and discovery in computational model-based inquiry activities. Instructional Science, 46(1), 35-60. https://doi.org/10.1007/s11251-017-9430-4
• Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55-62. https://doi.org/10.1145/2994591

APA

Amador-Rodríguez, R., Vieira, C., & Adúríz-Bravo, A. (2026). Science educators’ views on the role of computational thinking in scientific inquiry. Eurasia Journal of Mathematics, Science and Technology Education, 22(6), em2850. https://doi.org/10.29333/ejmste/18712

Vancouver

Amador-Rodríguez R, Vieira C, Adúríz-Bravo A. Science educators’ views on the role of computational thinking in scientific inquiry. EURASIA J Math Sci Tech Ed. 2026;22(6):em2850. https://doi.org/10.29333/ejmste/18712

AMA

Amador-Rodríguez R, Vieira C, Adúríz-Bravo A. Science educators’ views on the role of computational thinking in scientific inquiry. EURASIA J Math Sci Tech Ed. 2026;22(6), em2850. https://doi.org/10.29333/ejmste/18712

Chicago

Amador-Rodríguez, Rafael, Camilo Vieira, and Agustín Adúríz-Bravo. "Science educators’ views on the role of computational thinking in scientific inquiry". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 6 (2026): em2850. https://doi.org/10.29333/ejmste/18712

Harvard

Amador-Rodríguez, R., Vieira, C., and Adúríz-Bravo, A. (2026). Science educators’ views on the role of computational thinking in scientific inquiry. Eurasia Journal of Mathematics, Science and Technology Education, 22(6), em2850. https://doi.org/10.29333/ejmste/18712

MLA

Amador-Rodríguez, Rafael et al. "Science educators’ views on the role of computational thinking in scientific inquiry". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 6, 2026, em2850. https://doi.org/10.29333/ejmste/18712