LESSON STUDY BASED STEM ROBOTICS LEARNING FOR DEVELOPING EMOTIONAL INTELLIGENCE: TBLA AND RASCH MODEL PERSPECTIVES
DOI:
https://doi.org/10.22437/jiituj.v10i3.55313Keywords:
Collaborative Learning, Emotional Intelligence, Lesson Study, TBLA, NVivo, Rasch Model, Robotics Learning, STEM EducationAbstract
This study aimed to analyze students’ emotional intelligence development in STEM robotics learning through the Lesson Study framework integrated with Transcript-Based Lesson Analysis (TBLA), NVivo visualization, and Rasch Model analysis. This study employed a mixed-methods approach involving 23 junior high school students participating in robotics learning activities at SMPIT Nurul ‘Ilmi Jambi, Indonesia. The research was conducted in two Lesson Study cycles consisting of Plan–Do–See stages. Qualitative data were collected through classroom observations, audio-video recordings, and interaction transcripts analyzed using TBLA and NVivo software. Quantitative data were obtained through an emotional intelligence questionnaire and analyzed using the Rasch Model. The findings revealed significant improvements in students’ collaboration, communication, and emotional involvement from Cycle I to Cycle II. NVivo visualizations showed dominant emotional regulation expressions such as “wait,” “patient,” and “connect it first,” indicating the development of self-regulation, empathy, persistence, and teamwork during robotics troubleshooting activities. Rasch analysis further confirmed that students demonstrated a high level of emotional intelligence, with an average person measure of +1.19 logits and good model fit (Infit MNSQ = 1.03; Outfit MNSQ = 1.00). The study concludes that robotics learning within the Lesson Study framework effectively promotes students’ emotional intelligence through collaborative problem-solving and reflective interaction. The integration of TBLA, NVivo, and Rasch Model analysis provides a comprehensive framework for evaluating socio-emotional competencies in STEM education. The findings imply that STEM robotics learning should integrate emotional and collaborative dimensions as essential competencies for twenty-first century learners.
References
Amroh, Susanti, N., & Dani, R. (2025). Exploratuin of student activity in lesson study learning with transcript based lesson analysis using nvivo. EduFisika: Jurnal Pendidikan Fisika, 10(1), 11–23. https://doi.org/10.59052/edufisika.v10i1.38507
Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A systematic review of studies on educational robotics. Journal of Pre-College Engineering Education Research, 9(2), 19–42. https://doi.org/10.7771/2157-9288.1223
Aryadoust, V. (2021). Differential item functioning in while-listening performance tests: The case of International English Language Testing System (IELTS) listening module. International Journal of Listening, 26(1), 40–60. https://doi.org/10.1080/10904018.2012.639649.
Aryadoust, V., Ng, L. Y., & Sayama, H. (2019). A comprehensive review of Rasch measurement in educational assessment research. Educational Psychology Review, 31(3), 1–25. https://doi.org/10.1007/s10648-019-09489-0
Awsaf, A. A., Dou, R., Sunbury, S., Sonnert, G., & Sadler, P. (2026). A Structural Equation Model for Youths’ STEM Identity Development: Impact of Contextual STEM Learning Experiences. NARST Annual International Conference, Seattle, Washington., 1–15.
Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & education, 58(3), 978-988. https://doi.org/10.1016/j.compedu.2011.10.006.
Bond, T. G., Yan, Z., & Heene, M. (2021). Applying the Rasch model: Fundamental measurement in the human sciences (4th ed.). Routledge. https://doi.org/10.4324/9780429030499
Cardoso, L., Ponte, J. P. da, & Quaresma, M. (2025). The development of mathematical knowledge of prospective primary teachers in a lesson study. European Journal of Science and Mathematics Education, 13(4), 368–384. https://doi.org/https://doi.org/10.30935/scimath/17415.
Chalmers, C., Carter, M., Cooper, T., & Nason, R. (2017). Implementing robotics programs in early childhood settings: Effects on social and emotional learning. Australasian Journal of Early Childhood, 42(4), 69–76. https://doi.org/10.23965/AJEC.42.4.08.
Chang, Y. C., & Tsai, Y. T. (2022). The effect of university students’ emotional intelligence, learning motivation and self-efficacy on their academic achievement—Online English courses. Frontiers in Psychology, 13, 818929. https://doi.org/10.3389/fpsyg.2022.818929.
Ching, Y. H., Yang, D., Wang, S., Baek, Y., Swanson, S., & Chittoori, B. (2019). Elementary school student development of STEM attitudes and perceived learning in a STEM integrated robotics curriculum. TechTrends, 63(5), 590-601. DOI: https://doi.org/10.1007/s11528-019-00388-0.
Creswell, J., & Gregory, A. (2024). Collaborative reflection and peer monitoring in lesson study practices. Teaching and Teacher Education, 135, 104352.
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
Cheng, L., Antonenko, P. P., Ritzhaupt, A. D., & MacFadden, B. (2021). Exploring the role of 3D printing and STEM integration levels in students' STEM career interest. British Journal of Educational Technology, 52, 1262-1278. DOI: https://doi.org/10.1111/bjet.13077.
Dhyaaldian, S. M. A., Al-Zubaidi, S. H., Mutlak, D. A., Neamah, N. R., Albeer, A. A. M., Hamad, D. A., Al Hasani, S. F., Jaber, M. M., & Maabreh, H. G. (2022). Psychometric evaluation of cloze tests with the Rasch model. International Journal of Language Testing, 12(2), 95–106. https://doi.org/10.22034/ijlt.2022.157127.
Dignam, C., Smith, C. M., & Kelly, A. L. (2025). The heart and art of robotics: from AI to artificial emotional intelligence in STEM education. Journal of Education in Science, Environment and Health, 11(2), 151-169. https://doi.org/10.55549/jeseh.813.
Dudley, P. (2019). Lesson study: Professional learning for our time. Routledge. https://doi.org/10.4324/9781315148625
Eguchi, A. (2014, July). Robotics as a learning tool for educational transformation. In Proceeding of 4th international workshop teaching robotics, teaching with robotics & 5th international conference robotics in education Padova (Italy) (Vol. 1, pp. 1-6).
Fitriana, F., Widodo, A., & Suryana, Y. (2022). Transcript-based lesson analysis in collaborative learning: Exploring students’ classroom interaction patterns. Journal of Physics: Conference Series, 2193(1), 012045. https://doi.org/10.1088/1742-6596/2193/1/012045.
Goleman, D. (2017). Emotional intelligence: Why it can matter more than IQ (10th anniversary ed.). Bantam Books.
Hughes, B. E. (2018). Erratum: Coming out in STEM: Factors affecting retention of sexual minority STEM students. Science Advances, 4(6), 1–6. https://doi.org/10.1126/SCIADV.AAU2554.
Islami, A. P., Susanti, N., Dani, R., & City, J. (2025). Students’ scientific communication skills in STEM robotics learning through transcript-based lesson analysis (TBLA). Physics and Science Education Journal (PSEJ), 5(2), 72–79. https://doi.org/https://doi.org/10.30631/am3npq03
Johnson, R. B., Grove, A. L., & Clarke, A. (2020). Mixed methods research: Expanding the evidence base. American Behavioral Scientist, 64(6), 743–755. https://doi.org/10.1177/0002764219859633.
Jung, S. E., & Won, E. S. (2018). Systematic review of research trends in robotics education for young children. Sustainability, 10(4), 905. https://doi.org/10.3390/su10040905.
Keefer, K. V., Parker, J. D. A., & Saklofske, D. H. (2018). Keefer, K., Parker, J., & Saklofske, D. H. (2018). Emotional intelligence in education. Integrating Research with Practice. Cham (Suiza): Springer. https://doi.org/10.1007/978-3-319-90633-1.
Kong, S.-C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers & Education, 151, 1- 19. https://doi.org/10.1016/j.compedu.2020.103872.
Larssen, D., Cajkler, W., Mosvold, R., Bjuland, R., Helgevold, N., Fauskanger, J., Wood, P., Baldry, F., Jakobsen, A., Bugge, H., Næsheim-Bjørkvik, G., & Norton, J. (2018). A literature review of lesson study in initial teacher education: Perspectives about learning and observation. International Journal for Lesson & Learning Studies, 7(1), 8–22. https://doi.org/10.1108/IJLLS-06-2017-0030
Le, H. T. T., Pham, C. K., Le, N. C., Nguyen, N. T., Nguyen, N. T. L., Khuat, H. T. T., Nguyen, D. V., Vu, T. T., & Nguyen, T. T. T. (2025). STEM teaching competency of general education teachers: concept, definition, and theoretical framework. European Journal of STEM Education, 10(1), 1–17. https://doi.org/10.20897/ejsteme/17654.
Lewis, C., & Hurd, J. (2019). Lesson study step by step: How teacher learning communities improve instruction (2nd ed.). Heinemann.
Lewis, C., Friedkin, S., Emerson, K., Henn, L., & Goldsmith, L. (2019). How does lesson study work? Toward a theory of lesson study process and impact. In Theory and practice of lesson study in mathematics: An international perspective (pp. 13-37). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-04031-4_2.
Lewis, C., & Perry, R. (2017). Lesson study to scale up research-based knowledge: A randomized, controlled trial of fractions learning. Journal for Research in Mathematics Education, 48(3), 261–299. https://doi.org/10.5951/jresematheduc.48.3.0261.
MacCann, C., Jiang, Y., Brown, L. E. R., Double, K. S., Bucich, M., & Minbashian, A. (2020). Emotional intelligence predicts academic performance: A meta-analysis. Psychological Bulletin, 146(2), 150–186. https://doi.org/10.1037/bul0000219.
Miraz, M. H., Annamalah, S., & Sham, R. (2025). Integrating PLS-SEM and NVivo in mixed-methods educational research: A comprehensive evaluation of quantitative and qualitative analytical tools. Educational Process: International Journal, 19, e2025531. https://doi.org/10.22521/edupij.2025.19.531.
Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. (2016). Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. Journal of Research on Technology in Education, 48(1), 1–15. https://doi.org/10.1080/15391523.2015.1136648.
Ohnishi, Y., Honda, K., Nishioka, R., Mori, S., & Kawada, K. (2017). Robotics programming learning for elementary and junior high school students. Journal of Robotics and Mechatronics, 29(6), 992-998. https://doi.org/10.20965/jrm.2017.p0992.
Paulus, T. M., & Lester, J. N. (2022). Doing qualitative research in a digital world. Sage Publications.
Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage Publications.
Richit, A., Ponte, J. P., & Quaresma, M. (2021). Aprendizagens profissionais de professores evidenciadas em pesquisas sobre estudos de aula [Teachers’ professional learning revealed in lesson study research]. Bolema: Boletim de Educação Matemática, 35(70), 1107–1137. https://doi.org/10.1590/1980-4415v35n70 a26.
Sato, M. (2018). Spread and progress of School as Learning Community in Asia. In Lesson study and schools as learning communities (pp. 3-13). Routledge. https://doi.org/10.4324/9781315146843.
Saito, E., Khong, T. D. H., Hidayat, A., Hendayana, S., & Imansyah, H. (2020). Typologies of lesson study coordination: a comparative institutional analysis. Professional Development in Education, 46(1), 65–81. https://doi.org/10.1080/19415257.2018.1561495.
Sarkar Arani, M. R., Lander, B., Shibata, Y., Kim-Eng Lee, C., Kuno, H., & Lau, A. (2019). From “chalk and talk” to “guide on the side”: A cross-cultural analysis of pedagogy that drives customised teaching for personalised learning. European Journal of Education, 54(2), 233–249. https://doi.org/10.1111/ejed.12340.
Suryandai, K. C., Rokhmaniyah, Salimi, M., & Fatimah, S. (2022). Involvement of teachers, parents, and school committees in improving scientific attitudes of elementary school students: Application of Rasch model analysis. International Journal of Educational Methodology, 8(4), 783–794. https://doi.org/10.12973/ijem.8.4.783.
Susanti, N., Widodo, A., & Suryana, Y. (2023). Integrating TBLA and Rasch Model analysis in STEM robotics learning: Exploring students’ social interaction patterns. Journal of Physics: Conference Series, 2480(1), 012031. https://doi.org/10.1088/1742-6596/2480/1/012031.
Susanti, N., Fitriana, F., & Widodo, A. (2024). Emotional intelligence development through robotics learning integrated with lesson study. International Journal of STEM Education, 11(1), 45–58. https://doi.org/10.1186/s40594-024-00456-7.
Tan, A.-L., Ong, Y. S., Ng, Y. S., & Tan, J. H. J. (2022). STEM problem solving: inquiry, concepts, and reasoning. Science & Education, 0123456789. https://doi.org/10.1007/s11191-021-00310-2.
Tanti, T., Astalini, A., Darmaji, D., Kurniawan, D. A., & Fitriani, R. (2022). Student perception review from gender: electronic moduls of mathematical physics. JPI (Jurnal Pendidikan Indonesia), 11(1), 125-132. https://doi.org/10.23887/jpiundiksha.v11i1.35107.
Tanti, T., Utami, W., Deliza, D., & Jahanifar, M. (2025). Investigation in vocation high school for attitude and motivation students in learning physics subject. Journal Evaluation in Education (JEE), 6(2), 479-490. https://doi.org/10.37251/jee.v6i2.1452.
Teo, T. W., Tan, A. L., Ong, Y. S., & Choy, B. H. (2021). Centricities of STEM curriculum frameworks: Variations of the S-T-E-M Quartet. STEM Education, 1(3), 141. https://doi.org/10.3934/steme.2021011
Yani, M., Sembiring, B., Susanti, N., Study, L., Collaboration, S., & Kolaborasi, K. (2025). Lesson study: a qualitative analysis using nvivo in climate change course. Physics and Science Education Journal (PSEJ), 5(1), 1–9. https://doi.org/10.30631/psej.v5i1.3003.
Yang, D. & Baldwin, S.J. (2020). Using technology to support student learning in an integrated STEM learning environment. International Journal of Technology in Education and Science (IJTES), 4(1), 1-11. https://doi.org/10.46328/ijtes.v4i1.22.
Zviel-Girshin, R., & Rosenberg, H. (2025). Emotional intelligence and collaborative learning in technology-enhanced STEM education. International Journal of STEM Education, 12(1), 1–15. https://doi.org/10.1186/s40594-025-00512-3.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nova Susanti, Kharisma Nur Kholifah, Neneng Lestari
This work is licensed under a Creative Commons Attribution 4.0 International License.
