Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study
Full Text (PDF)
M. Anas Thohir 1 * , Nurul Istiq’faroh 2 , Awaluddin Hidayat Ramli Inaku 3 , Anisa Wahyu Kusumaningtyas 4 , Weahason Weahama 5
More Detail
1 Department of Elementary and Preschool Education, Universitas Negeri Malang, Malang, INDONESIA2 Faculty of Education, Universitas Negeri Surabaya, Surabaya, INDONESIA3 Department of Environmental Science, Faculty of Engineering and Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, INDONESIA4 Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, INDONESIA5 Faculty of Education, Prince of Songkla University, Songkhla, THAILAND* Corresponding Author
Abstract
Educational robotics is increasingly used to develop pre-service elementary teachers’ computational thinking (CT), yet limited empirical evidence exists on the competencies required for robotics-integrated teaching. This exploratory sequential mixed-methods study identified robotics-related perceived competencies and examined the relationships among CT, technological pedagogical content knowledge (TPACK), and robotics pedagogical content knowledge (RPCK). Qualitative data from 128 pre-service teachers were analyzed using inductive thematic analysis, followed by a survey of 150 participants analyzed with partial least squares structural equation modeling. The qualitative phase identified 22 perceived competencies grouped into CT, TPACK, RPCK, and complementary professional domains, with digital technology literacy, creativity, and problem-solving emerging most frequently. The quantitative results showed that abstraction and algorithmic thinking were associated with TPACK, whereas decomposition and debugging were associated with RPCK. TPACK showed the strongest association with RPCK. Generalization was excluded from the structural analysis because of insufficient discriminant validity. These findings provide an exploratory and empirically informed mapping of pre-service teachers’ perceived competencies robotics-integrated science teaching and preliminary evidence of associations among selected dimensions of CT, TPACK, and RPCK. The complementary and emergent domain identified in the qualitative phase requires further quantitative examination.
License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 22, Issue 12, December 2026, Article No: em2963
https://doi.org/10.29333/ejmste/19895
Publication date: 08 Nov 2026
Article Views: 13
Article Downloads: 10
Open Access References How to cite this articleReferences
- Ahmichane, J., Jhilal, A., Hadder, Y., & El Mallahi, M. (2025). Validating a TPACK instrument in the Moroccan context: A four-factor model for preservice mathematics and sciences teachers. International Journal of Environment, Engineering and Education, 7(2), 158-167. https://doi.org/10.55151/ijeedu.v7i2.294
- Alam, A., & Mohanty, A. (2024). Integrated constructive robotics in education (ICRE) model: A paradigmatic framework for transformative learning in educational ecosystem. Cogent Education, 11(1), Article 232487. https://doi.org/10.1080/2331186X.2024.2324487
- Angeli, C. (2022). The effects of scaffolded programming scripts on pre-service teachers’ computational thinking: Developing algorithmic thinking through programming robots. International Journal of Child-Computer Interaction, 31, Article 100329. https://doi.org/10.1016/j.ijcci.2021.100329
- Angeli, C., & Jaipal-Jamani, K. (2018). Preparing pre-service teachers to promote computational thinking in school classrooms. In M. Khine (Ed.), Computational thinking in the STEM disciplines (pp. 127-150). Springer. https://doi.org/10.1007/978-3-319-93566-9_7
- Angeli, C., & Valanides, N. (2013). Technology mapping: An approach for developing technological pedagogical content knowledge. Journal of Educational Computing Research, 48(2), 199-221. https://doi.org/10.2190/EC.48.2.e
- Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Educational Technology & Society, 3(19), 47-57.
- Avcı, C., & Deniz, M. N. (2022). Computational thinking: Early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy. Education and Information Technologies, 27(8), 11689-11713. https://doi.org/10.1007/s10639-022-11078-5
- Bai, S., & Tian, P. (2025). Educational robotics may enhance students’ conceptual knowledge, applied skills, and learning attitude in STEM education: A meta-analysis. Educational Technology and Society, 28(4), 274-303. https://doi.org/10.30191/ETS.202510_28(4).SP06
- Bakala, E., Gerosa, A., Hourcade, J. P., & Tejera, G. (2021). Preschool children, robots, and computational thinking: A systematic review. International Journal of Child-Computer Interaction, 29, Article 100337. https://doi.org/10.1016/j.ijcci.2021.100337
- Barker, C., Pistrang, N., & Elliott, R. (2015). Research methods in clinical psychology: An introduction for students and practitioners (3rd ed.). John Wiley & Sons. https://doi.org/10.1002/9781119154082
- Bellas, F., Naya-Varela, M., Mallo, A., & Paz-Lopez, A. (2024). Education in the AI era: A long-term classroom technology based on intelligent robotics. Humanities and Social Sciences Communications, 11, Article 1425. https://doi.org/10.1057/s41599-024-03953-y
- 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
- Blair, J., Czaja, R. F., & Blair, E. A. (2013). Designing surveys: A guide to decisions and procedures. SAGE. https://doi.org/10.4135/9781071909904
- Bou Saad, R., Garcia, A. L., & Garcia, J. M. C. (2025). Mapping constructivist active learning for STEM: Toward sustainable education. Sustainability, 17(13), Article 6225. https://doi.org/10.3390/su17136225
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
- Budiyanto, C. W., Fenyvesi, K., Lathifah, A., & Yuana, R. A. (2022). Computational thinking development: Benefiting from educational robotics in STEM teaching. European Journal of Educational Research, 11(4), 1997-2012. https://doi.org/10.12973/eu-jer.11.4.1997
- Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
- Chandra, Y., & Shang, L. (2019). Inductive coding. In Qualitative research using R: A systematic approach (pp. 91-106). Springer. https://doi.org/10.1007/978-981-13-3170-1_8
- Cheng, L., Wang, X., & Ritzhaupt, A. D. (2023). The effects of computational thinking integration in STEM on students’ learning performance in K-12 education: A meta-analysis. Journal of Educational Computing Research, 61(2), 416-443. https://doi.org/10.1177/07356331221114183
- Ching, Y.-H., & Hsu, Y.-C. (2024). Educational robotics for developing computational thinking in young learners: A systematic review. TechTrends, 68(3), 423-434. https://doi.org/10.1007/s11528-023-00841-1
- Christensen, R., Knezek, G., & Tyler-Wood, T. (2015). Alignment of hands-on STEM engagement activities with positive STEM dispositions in secondary school students. Journal of Science Education and Technology, 24(6), 898-909. https://doi.org/10.1007/s10956-015-9572-6
- Chu, Y.-K., Liang, J.-C., & Tsai, M.-J. (2020). Elementary and middle school teachers’ teaching attitude, belief, motivation and technological pedagogical content knowledge of robotics-based instruction. International Journal on Digital Learning Technology, 12(2), 75-97.
- Cohen, J. (1988). Statistical power analysis for the behavioural sciences. Lawrence Erlbaum Associates.
- Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. In Handbook of mixed methods in social and behavioral research (pp. 209-240). SAGE.
- Crompton, H., & Sykora, C. (2021). Developing instructional technology standards for educators: A design-based research study. Computers and Education Open, 2, Article 100044. https://doi.org/10.1016/j.caeo.2021.100044
- Dolgopolovas, V., & Dagiene, V. (2024). Competency-based TPACK approaches to computational thinking and integrated STEM: A conceptual exploration. Computer Applications in Engineering Education, 32(6), Article e22788. https://doi.org/10.1002/cae.22788
- Dong, S., Zhi, R., & Gan, F. (2025). Integrating robotics in art education: Leveraging constructivist and experiential learning frameworks to enhance cognitive development, problem-solving skills and collaboration among diverse learners. European Journal of Education, 60(2), Article e70114. https://doi.org/10.1111/ejed.70114
- Dong, W., Li, Y., Sun, L., & Liu, Y. (2024). Developing pre-service teachers’ computational thinking: A systematic literature review. International Journal of Technology and Design Education, 34(1), 191-227. https://doi.org/10.1007/s10798-023-09811-3
- Eguchi, A. (2014). Educational robotics for promoting 21st century skills. Journal of Automation, Mobile Robotics and Intelligent Systems, 8(1), 5-11. https://doi.org/10.14313/jamris_1-2014/1
- Eguchi, A. (2022). AI-powered educational robotics as a learning tool to promote artificial intelligence and computer science education. In M. Merdan, W. Lepuschitz, G. Koppensteiner, R. Balogh, & D. Obdržálek (Eds.), Robotics in education. RiE 2021. Advances in intelligent systems and computing, vol 1359 (pp. 279-287). Springer. https://doi.org/10.1007/978-3-030-82544-7_26
- Eguchi, A. (2023). Machine learning application with AI-robotics tool – What we learned from pilot studies In J. M. Cascalho, M. O. Tokhi, M. F. Silva, A. Mendes, K. Goher, & M. Funk (Eds.), Robotics in natural settings. CLAWAR 2022. Lecture notes in networks and systems, vol 530 (pp. 383-394). Springer. https://doi.org/10.1007/978-3-031-15226-9_36
- Falloon, G. (2024). Advancing young students computational thinking: An investigation of structured curriculum in early years primary schooling. Computers and Education, 216, Article 105045. https://doi.org/10.1016/j.compedu.2024.105045
- Fereday, J., Adelaide, N., Australia, S., & Eimear Muir-Cochrane, A. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 1(5), 80-92. https://doi.org/10.1177/160940690600500107
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
- Fung, K. Y., Fung, K. C., Lui, T. L. R., Sin, K. F., Lee, L. H., Qu, H., & Song, S. (2025). Exploring the impact of robot interaction on learning engagement: A comparative study of two multi-modal robots. Smart Learning Environments, 12, Article 12. https://doi.org/10.1186/s40561-024-00362-1
- Gonscherowski, P., & Rott, B. (2025). A systematic review of the literature on TPACK instruments used with pre-service teachers from 2017 to 2023 focused on selecting digital resources. Journal of Computers in Education, 13(3), 945-982. https://doi.org/10.1007/s40692-025-00379-6
- Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
- Haddadin, S. (2024). The Franka Emika robot: A standard platform in robotics research. IEEE Robotics & Automation Magazine, 31(4), 136-148. https://doi.org/10.1109/MRA.2024.3451788
- Haidegger, T., Mai, V., Mörch, C. M., Boesl, D. O., Jacobs, A., Rao R, B., Khamis, A., Lach, L., & Vanderborght, B. (2023). Robotics: Enabler and inhibitor of the sustainable development goals. Sustainable Production and Consumption, 43, 422-434. https://doi.org/10.1016/j.spc.2023.11.011
- Hair, J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising, 46(1), 163-177. https://doi.org/10.1080/00913367.2017.1281777
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
- Henze, J., Schatz, C., Malik, S., & Bresges, A. (2022). How might we raise interest in robotics, coding, artificial intelligence, STEAM and sustainable development in university and on-the-job teacher training? Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.872637
- Hosseini, Z. (2015). Development of technological pedagogical content knowledge through constructionist activities. Procedia-Social and Behavioral Sciences, 182, 98-103. https://doi.org/10.1016/j.sbspro.2015.04.743
- Hsu, C.-Y., & Tsai, M.-J. (2024). Predicting robotics pedagogical content knowledge: The role of computational and design thinking dispositions via teaching beliefs. Journal of Educational Computing Research, 62(5), 1159-1181. https://doi.org/10.1177/07356331241236882
- Hsu, T.-C., & Chen, M.-S. (2025). Effects of students using different learning approaches for learning computational thinking and AI applications. Education and Information Technologies, 30(6), 7549-7571. https://doi.org/10.1007/s10639-024-13116-w
- Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7
- Isnaini, R., Budiyanto, C., & Widiastuti, I. (2019). Robotics-based learning to support computational thinking skills in early childhood. AIP Conference Proceedings, 2194(1), Article 020044. https://doi.org/10.1063/1.5139776
- Jamal, N. N., Jawawi, D. N. A., Hassan, R., Mohamad, R., Halim, S. A., Saadon, N. A., Isa, M. A., & Hamed, H. N. A. (2024). Adaptive learning framework for learning computational thinking using educational robotics. Computer Applications in Engineering Education, 32(4), Article e22732. https://doi.org/10.1002/cae.22732
- Kerimbayev, N., Nurym, N., Akramova, A., & Abdykarimova, S. (2023). Educational robotics: Development of computational thinking in collaborative online learning. Education and Information Technologies, 28(11), 14987-15009. https://doi.org/10.1007/s10639-023-11806-5
- Kert, S. B., Erkoç, M. F., & Yeni, S. (2020). The effect of robotics on six graders’ academic achievement, computational thinking skills and conceptual knowledge levels. Thinking Skills and Creativity, 38, Article 100714. https://doi.org/10.1016/j.tsc.2020.100714
- Kim, C., Belland, B. R., Vasconcelos, L., & Hill, R. B. (2024). Playful programming, social resilience, and persistent actions as drivers of preservice early childhood teachers’ engagement in computer science. Sage Open, 14(4). https://doi.org/10.1177/21582440241284488
- Kong, S. C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers and Education, 151, Article 103872. https://doi.org/10.1016/j.compedu.2020.103872
- Kong, S.-C., & Lai, M. (2022). A proposed computational thinking teacher development framework for K-12 guided by the TPACK model. Journal of Computers in Education, 9(3), 379-402. https://doi.org/10.1007/s40692-021-00207-7
- Kong, S.-C., Lai, M., & Li, Y. (2023). Scaling up a teacher development programme for sustainable computational thinking education: TPACK surveys, concept tests and primary school visits. Computers & Education, 194, Article 104707. https://doi.org/10.1016/j.compedu.2022.104707
- Lampropoulos, G. (2025). Social robots in education: Current trends and future perspectives. Information, 16(1), Article 29. https://doi.org/10.3390/info16010029
- Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
- Lane, W. B., Galanti, T. M., & Rozas, X. L. (2023). Teacher re-novicing on the path to integrating computational thinking in high school physics instruction. Journal for STEM Education Research, 6(2), 302-325. https://doi.org/10.1007/s41979-023-00100-1
- Lee, B., Ku, S., & Ko, K. (2025). AI robots promote South Korean preschoolers’ AI literacy and computational thinking. Family Relations, 74(3), 1354-1375. https://doi.org/10.1111/fare.13189
- Li, Q. (2021). Computational thinking and teacher education: An expert interview study. Human Behavior and Emerging Technologies, 3(2), 324-338. https://doi.org/10.1002/hbe2.224
- Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3(2), 147-166. https://doi.org/10.1007/s41979-020-00044-w
- Lu, S., Nuryana, Z., Ni, X., Xu, W., & Alias, M. N. (2025). A decade of educational robotics: Trends and SDG contributions. Humanities and Social Sciences Communications, 12, Article 1356. https://doi.org/10.1057/s41599-025-05663-5
- M. Thamrin, N., Irawan, A., Zulkifli, Z., Al Junid, S. A. M., Megat Ali, M. S. A., & P. P. Abdul Majeed, A. (2026). Robotics and artificial intelligence: Autonomous learning in education. In Robotics in education. SpringerBriefs in applied sciences and technology (pp. 55-72). Springer. https://doi.org/10.1007/978-981-95-3548-4_5
- Macann, V., Yadav, A., Ukkonen, A., Lee, H., & D’Amico, A. (2026). Computational thinking integrated science: How professional development can support elementary teachers. Journal of Computers in Education, 13(2), 735-767. https://doi.org/10.1007/s40692-025-00370-1
- MacCallum, K. (2025). Integrating computational thinking through digital creation: The (TPAC)2K model. Teaching and Teacher Education, 161, Article 105056. https://doi.org/10.1016/j.tate.2025.105056
- Martínez Rojas, E., Valencia-Arias, A., Vásquez Coronado, M. H., Barandiarán Gamarra, J. M., Agudelo-Ceballos, E., Benjumea-Arias, M. L., & Vega Mori, L. (2025). Educational robotics for primary education: An analysis of research trends. Eurasia Journal of Mathematics, Science and Technology Education, 21(3), Article em2602. https://doi.org/10.29333/ejmste/16050
- Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
- Moore, A. M., Fairhurst, P., Correia, C. F., Harrison, C., & Bennett, J. M. (2020). Science practical work in a COVID-19 world: Are teacher demonstrations, videos and textbooks effective replacements for hands-on practical activities? School Science Review, 102(378), 7-12.
- Moreno Palma, N., Berral Ortiz, B., Fernández Fernández, C. R., & Victoria Maldonado, J. J. (2025). Pensamiento computacional en la educación básica a través de la robótica educativa: Una revisión sistemática [Computational thinking in basic education through educational robotics: A systematic review]. European Public & Social Innovation Review, 10, 1-20. https://doi.org/10.31637/epsir-2025-1099
- Nannim, F. A., Ibezim, N. E., Oguguo, B. C. E., & Nwangwu, E. C. (2024). Effect of project-based Arduino robot application on trainee teachers computational thinking in robotics programming course. Education and Information Technologies, 29(10), 13155-13170. https://doi.org/10.1007/s10639-023-12380-6
- Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463-484. https://doi.org/10.1007/s11423-019-09708-w
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
- Okundaye, O., Natarajarathinam, M., Qiu, S., Kuttolamadom, M. A., Chu, S., & Quek, F. (2022). Making STEM real: The design of a making-production model for hands-on STEM learning. European Journal of Engineering Education, 47(6), 1122-1143. https://doi.org/10.1080/03043797.2022.2121685
- Pajchel, K., Mifsud, L., Frågåt, T., Rehder, M. M., Juuti, K., Bogar, Y., Lavonen, J., Schrøder, V., Aalbergsjø, S. G., & Rognes, A. (2024). Sign of the times: The framing of computational thinking in Danish, Finnish, and Norwegian curricula. Nordic Journal of Comparative and International Education, 8(4). https://doi.org/10.7577/njcie.5744
- Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. Basic Books.
- Papert, S. (2020). Mindstorms: Children, computers, and powerful ideas. Basic Books.
- Papert, S., & Harel, I. (2002). Situating constructionism. In Proceedings of the INCAE.
- Proctor, C. (2023). Computational thinking. In International encyclopedia of education (pp. 88-95). Elsevier. https://doi.org/10.1016/B978-0-12-818630-5.13078-7
- Puzio, A. (2025). Robot, let us pray! Can and should robots have religious functions? An ethical exploration of religious robots. AI & SOCIETY, 40(2), 1019-1035. https://doi.org/10.1007/s00146-023-01812-z
- Qu, J. R., & Fok, P. K. (2022). Cultivating students’ computational thinking through student-robot interactions in robotics education. International Journal of Technology and Design Education, 32(4), 1983-2002. https://doi.org/10.1007/s10798-021-09677-3
- Saritepeci, M. (2022). Modelling the effect of TPACK and computational thinking on classroom management in technology enriched courses. Technology, Knowledge and Learning, 27(4), 1155-1169. https://doi.org/10.1007/s10758-021-09529-y
- Scaradozzi, D., Screpanti, L., & Cesaretti, L. (2019). Towards a definition of educational robotics: A classification of tools, experiences and assessments. In L. Daniela (Ed.), Smart learning with educational robotics (pp. 63-92). Springer. https://doi.org/10.1007/978-3-030-19913-5_3
- Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological pedagogical content knowledge (TPACK): The development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 123-149. https://doi.org/10.1080/15391523.2009.10782544
- Setiyawan, A., Soeharto, S., Wijaya, T. T., Korenova, L., & Lavicza, Z. (2025). Measuring teachers’ competencies for AI integration: Development and validation of the AI-TPACK in vocational education. Computers and Education Open, 9, Article 100319. https://doi.org/10.1016/j.caeo.2025.100319
- Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Education Review, 57(1), 1-21. https://doi.org/10.17763/haer.57.1.j463w79r56455411
- Slavin, R. E. (2018). Educational psychology: Theory and practice (12th ed.). Pearson.
- Su, W.-S., Hwang, G.-J., Yin, C., & Chang, C.-Y. (2025). Exploring gender and educational background effects on 21st-century competencies in robot-integrated STEM education: The role of learning attitudes and satisfaction. Educational Technology and Society, 28(4), 394-410. https://doi.org/10.30191/ETS.202510_28(4).SP11
- Umutlu, D. (2022). TPACK leveraged: A redesigned online educational technology course for STEM preservice teachers. Australasian Journal of Educational Technology, 38(3), 99-116. https://doi.org/10.14742/ajet.4773
- Vasconcelos, L., Arslan-Ari, I., Miller, B., & Gonzalez-Tapia, R. (2025). Robotics in early childhood STEM education (REC-STEMEd): The impact on preservice teachers’ attitudes and intentions toward computational thinking. Education and Information Technologies, 30(13), 18347-18374. https://doi.org/10.1007/s10639-025-13529-1
- Wang, C., Shen, J., & Chao, J. (2022). Integrating computational thinking in STEM education: A literature review. International Journal of Science and Mathematics Education, 20(8), 1949-1972. https://doi.org/10.1007/s10763-021-10227-5
- Wang, H., Luo, N., Zhou, T., & Yang, S. (2024). Physical robots in education: A systematic review based on the technological pedagogical content knowledge framework. Sustainability, 16(12), Article 4987. https://doi.org/10.3390/su16124987
- Wang, W., & Shen, G. (2026). Educational robotics and student development: A second-order meta-analysis. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2025.2598241
- Williams, M., & Moser, T. (2019). The art of coding and thematic exploration in qualitative research. International Management Review, 15, Article 45.
- Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
- Wing, J. M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. https://doi.org/10.17471/2499-4324/922
- Woolfolk, A. (2016). Educational psychology (13th ed.). Pearson.
- Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), Article 5. https://doi.org/10.1145/2576872
- 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
- Yang, S., Baird, M., O’Rourke, E., Brennan, K., & Schneider, B. (2024). Decoding debugging instruction: A systematic literature review of debugging interventions. ACM Transactions on Computing Education, 24(4), 1-44. https://doi.org/10.1145/3690652
- Yang, W. (2025). A three-phase professional development approach to improving robotics pedagogical knowledge and computational thinking attitude of early childhood teachers. Computers & Education, 231, Article 105282. https://doi.org/10.1016/j.compedu.2025.105282
- Yang, Y., Long, Y., Sun, D., Van Aalst, J., & Cheng, S. (2020). Fostering students’ creativity via educational robotics: An investigation of teachers’ pedagogical practices based on teacher interviews. British Journal of Educational Technology, 51(5), 1826-1842. https://doi.org/10.1111/bjet.12985
- Yanış, H., & Yürük, N. (2021). Development, validity, and reliability of an educational robotics based technological pedagogical content knowledge self-efficacy scale. Journal of Research on Technology in Education, 53(4), 375-403. https://doi.org/10.1080/15391523.2020.1784065
- Zollman, A. (2012). Learning for STEM literacy: STEM literacy for learning. School Science and Mathematics, 112(1), 12-19. https://doi.org/10.1111/j.1949-8594.2012.00101.x
- Zviel-Girshin, R., & Rosenberg, N. (2025). Enhancing early STEM engagement: The impact of inquiry-based robotics projects on first-grade students’ problem-solving self-efficacy and collaborative attitudes. Education Sciences, 15(10), Article 1404. https://doi.org/10.3390/educsci15101404
How to cite this article
APA
Thohir, M. A., Istiq’faroh, N., Inaku, A. H. R., Kusumaningtyas, A. W., & Weahama, W. (2026). Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study. Eurasia Journal of Mathematics, Science and Technology Education, 22(12), em2963. https://doi.org/10.29333/ejmste/19895
Vancouver
Thohir MA, Istiq’faroh N, Inaku AHR, Kusumaningtyas AW, Weahama W. Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study. EURASIA J Math Sci Tech Ed. 2026;22(12):em2963. https://doi.org/10.29333/ejmste/19895
AMA
Thohir MA, Istiq’faroh N, Inaku AHR, Kusumaningtyas AW, Weahama W. Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study. EURASIA J Math Sci Tech Ed. 2026;22(12), em2963. https://doi.org/10.29333/ejmste/19895
Chicago
Thohir, M. Anas, Nurul Istiq’faroh, Awaluddin Hidayat Ramli Inaku, Anisa Wahyu Kusumaningtyas, and Weahason Weahama. "Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 12 (2026): em2963. https://doi.org/10.29333/ejmste/19895
Harvard
Thohir, M. A., Istiq’faroh, N., Inaku, A. H. R., Kusumaningtyas, A. W., and Weahama, W. (2026). Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study. Eurasia Journal of Mathematics, Science and Technology Education, 22(12), em2963. https://doi.org/10.29333/ejmste/19895
MLA
Thohir, M. Anas et al. "Mapping educational robotics competencies for pre-service teachers in science course: An exploratory sequential mixed-methods study". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 12, 2026, em2963. https://doi.org/10.29333/ejmste/19895