Comparing LEGO Spike and mBot Robot: Preliminary Data on Math and Science Enjoyment and Self-Efficacy among Lower Secondary School Students

Authors

  • Monica Pivetti
  • Davide Clemente
  • Claudia Russo
  • Virginia Dusi
  • Luciano Romano

Keywords:

Educational robotics; Self-efficacy; Math; Science; STEM; emotions; mediation pathway

Abstract

Although Educational robotics (ER) has been increasingly adopted in STEM education to foster both cognitive and motivational outcomes, limited research has compared the effects of different robotics kits on students’ self-efficacy. Additionally, little is known about the possible mediating role of positive emotions in this process. This quasi-experimental study has preliminarily investigated whether two ER kits, namely mBot and LEGO Spike Prime, could be associated with different levels of students’ self-efficacy in STEM. Also, this study has exploredwhether enjoyment in math and science lessons could mediate these relationships. Sixty-six junior secondary school Italian students participated in robotics-based activities integrated in regular school lessons for 10 hours altogether, using LEGO Spike prime or mBot kit. Students filled in an online questionnaire before and after their participation, measuring self-efficacy and enjoyment via two items each. No statistically significant pre-post change was observed. Mediation analyses with 95%CI percentile bootstrap (5000 resamples) showed that students using LEGO Spike Prime reported higher enjoyment in science lessons post-intervention, which in turn predicted greater self-efficacy in science homework post-intervention (partially standardized regression coefficient = .21, BootSE = .11; 95% BootCI [.019 to .458]). In contrast, participation in mBot activities was directly associated with higher self-efficacy in math homework, without a mediating role of enjoyment. These preliminary findings provide some provisional indications about the relevance of choosing one robotic kit as opposed to another during ER activities, in order to maximize subject- specific self-efficacy. Also, results underscore the importance of considering both cognitive and affective (i.e., emotions) dimensions of learning when implementing ER activities in school settings.

https://doi.org/10.26803/ijlter.25.9.15

References

Agatolio, F., Moro, M., Menegatti, E., & Pivetti, M. (2019). A critical reflection on the expectations about the impact of educational robotics on problem-solving capability. In M. Strand, R. Dillmann, E. Menegatti, & S. Ghidoni (Eds.), Intelligent autonomous systems 15 (Advances in Intelligent Systems and Computing, Vol. 867, pp. 877–888). Springer. https://doi.org/10.1007/978-3-030-01370-7_68

Agus, M., Marras, A., & Negrini, L. (2025). The Italian Version of the Robotics Learning Self-Efficacy Scale (RLSES-IT): Assessment of psychometric features in a sample of young students. Social Sciences & Humanities Open, 11, 101646. https://doi.org/10.1016/j.ssaho.2025.101646

Akman Selcuk, N., Küçük, S., & ?i?man, B. (2024). Does really educational robotics improve secondary school students' course motivation, achievement and attitude? Education and Information Technologies, 29, 23753–23780. https://doi.org/10.1007/s10639-024-12773-1

Alimisis, D., Moro, M., Arlegui, J., Pina, A., Frangou, S., & Papanikolaou, K. (2007). Robotics and constructivism in education: The TERECoP project. In I. Kalaš (Ed.), EuroLogo 2007: 40 years of influence on education. Proceedings of the 11th European Logo Conference (pp. 19–24). Comenius University.

An, F., Xi, L., Yu, J., & Zhang, M. (2022). Relationship between technology acceptance and self-directed learning: Mediation role of positive emotions and technological self-efficacy. Sustainability, 14(16), 10390. https://doi.org/10.3390/su141610390

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 (J-PEER), 9(2), Article 2. https://doi.org/10.7771/2157-9288.1223

Atman Uslu, N., Yavuz, G. Ö., & Kocak Usluel, Y. (2023). A systematic review study on educational robotics and robots. Interactive Learning Environments, 31(9), 5874-5898. https://doi.org/10.1080/10494820.2021.2023890

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.

Bano, S., Atif, K., & Mehdi, S. A. (2024). Systematic review: Potential effectiveness of educational robotics for 21st century skills development in young learners. Education and Information Technologies, 29(9), 11135-11153. https://doi.org/10.1007/s10639-023-12233-2

Barbaranelli, C. (2007). Analisi dei dati: Tecniche multivariate per la ricerca psicologica e sociale. Led Edizioni Universitarie.

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

Benitti, F. B. V., & Spolaôr, N. (2017). How have robots supported STEM teaching? In M. Khine (Ed.), Robotics in STEM education (pp. 103-129). Springer. https://doi.org/10.1007/978-3-319-57786-9_5

Beug, A. (2012). Teaching introductory programming concepts: A comparison of Scratch and Arduino (Master’s thesis, California Polytechnic State University). https://doi.org/10.15368/theses.2012.95.

Bonaiuti, G., Campitiello, L., Di Tore, S., & Marras, A. (2022). Educational robotics studies in Italian scientific journals: A systematic review. Frontiers in Education, 7, 1005669. https://doi.org/10.3389/feduc.2022.1005669

Burack, C., Melchior, A., & Hoover, M. (2019). Do after-school robotics programs expand the pipeline into STEM majors in college? Journal of Pre-College Engineering Education Research (J-PEER), 9(2), Article 7. https://doi.org/10.7771/2157-9288.1244

Chiang, F. K., Zhang, Y., Zhu, D., Shang, X., & Jiang, Z. (2022). The influence of online STEM education camps on students’ self-efficacy, computational thinking, and task value. Journal of Science Education and Technology, 31(4), 461-472. https://doi.org/10.1007/s10956-022-09967-y

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

Crane, M., Bohn-Goldbaum, E., Grunseit, A., & Bauman, A. (2020). Using natural experiments to improve public health evidence: A review of context and utility for obesity prevention. Health Research Policy and Systems, 18, 1-13. https://doi.org/10.1186/s12961-020-00564-2

Crowder, A., & Whittle, K. (2022). Increasing middle school girls’ STEM self-efficacy with soldering, robots, and Mobius strips. International Journal of Gender, Science and Technology, 14(2), 232-246.

Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.

Fanchamps, N. L., Slangen, L., Hennissen, P., & Specht, M. (2021). The influence of SRA programming on algorithmic thinking and self-efficacy using LEGO robotics in two types of instruction. International Journal of Technology and Design Education, 31(2), 203-222. https://doi.org/10.1007/s10798-019-09559-9

Fagin, B. S., & Merkle, L. (2002). Quantitative analysis of the effects of robots on introductory computer science education. Journal on Educational Resources in Computing, 2(4), 1–17. https://doi.org/10.1145/949257.949259

Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digital Health, 4(4), e0000764. https://doi.org/10.1371/journal.pdig.0000764

Ferrarelli, P., & Iocchi, L. (2021). Learning Newtonian physics through programming robot experiments. Technology, Knowledge and Learning, 26(4), 789-824. https://doi.org/10.1007/s10758-021-09508-3

Fletcher, K., Wright, C. A., Pesch, A., Abdurokhmonova, G., & Hirsh-Pasek, K. (2024). Active playful learning as a robust, adaptable, culturally relevant pedagogy to foster children’s 21st-century skills. Journal of Children and Media, 18(3), 309-321.

Ford, M. M., Usinger, J., & Smith, S. (2023). Failure mindset and robotics self-efficacy in STEM learning environments. Journal of Educational Technology Systems, 52(1), 3–25. https://doi.org/10.1177/00472395231167364

Ford, C. J., Usher, E. L., Scott, V. L., & Chen, X. Y. (2023). The ‘perfect’ lens: Perfectionism and early adolescents' math self?efficacy development. British Journal of Educational Psychology, 93(1), 211-228. https://doi.org/10.1111/bjep.12550

Fortunati, L., Esposito, A., Ferrin, G., & Viel, M. (2014). Approaching social robots through playfulness and doing-it-yourself: Children in action. Cognitive Computation, 6(4), 789-801. https://doi.org/10.1007/s12559-014-9303-y

Fortunati, L., Manganelli, A. M., & Ferrin, G. (2022). Arts and crafts robots or LEGO® MINDSTORMS robots? A comparative study in educational robotics. International Journal of Technology and Design Education, 32(1), 287-310. https://doi.org/10.1007/s10798-020-09609-7

Graesser, A. C. (2020). Emotions are the experiential glue of learning environments in the 21st century. Learning and Instruction, 70, 101212. https://doi.org/10.1016/j.learninstruc.2019.05.009

Grani?, A. (2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27, 9725–9744. https://doi.org/10.1007/s10639-022-10951-7

Gümü?, M. M., Kayhan, O., Korkmaz, Ö., Altun, H., & Y?lmaz, N. (2023). Pedagogical classification of educational robots in pre-school teaching. International Online Journal of Primary Education, 12(1), 25–41. https://doi.org/10.55020/iojpe.1177237

Gunes, H., & Kucuk, S. (2022). A systematic review of educational robotics studies for the period 2010–2021. Review of Education, 10(3), e3381. https://doi.org/10.1002/rev3.3381

Hangün, M. E., & Türel, Y. K. (2025). The effects of robot programming on mathematical achievement, mathematics anxiety, and programming self-efficacy. Computer Applications in Engineering Education, 33(3), e70030. https://doi.org/10.1002/cae.70030

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium. Communication Monographs, 76(4), 408–420. https://doi.org/10.1080/03637750903310360

Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4-40. https://doi.org/10.1080/03637751.2017.1352100

Himawan, S. B., Budiyanto, C. W., & Budianto, A. (2024). Technology self-efficacy in robotic-based integrated STEM learning: A systematic review. In Proceedings of the 6th Vocational Education International Conference (VEIC 2024) (Vol. 896, p. 135). Springer Nature. https://doi.org/10.2991/978-2-38476-342-9_18

Hong, L. (2024). The impact of educational robots on students’ computational thinking: A meta-analysis of K-12. Education and Information Technologies, 29, 13813–13838. https://doi.org/10.1007/s10639-023-12415-y

Isabell, T., & Mentzer, N. (2025). Analysis of Student Self-Efficacy during an Integrated, Life-Centered Robotics Learning Experience. Journal of Technology Education, 37(1), 68-93. https://doi.org/10.21061/jte.v37i1.a.4

Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The development of the STEM Career Interest Survey (STEM-CIS). Research in Science Education, 44(3), 461-481. https://doi.org/10.1007/s11165-013-9389-3

Kirk, B. A., Schutte, N. S., & Hine, D. W. (2008). Development and preliminary validation of an emotional self-efficacy scale. Personality and Individual Differences, 45(5), 432-436.

Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267-277. https://doi.org/10.1177/1365480216659733

Korkmaz, Ö. (2016). The effect of LEGO Mindstorms EV3 based design activities on students’ attitudes towards learning computer programming, self-efficacy beliefs and levels of academic achievement. Baltic Journal of Modern Computing, 4(4), 994–1007. https://eric.ed.gov/?id=ED582973

Koster, R. (2013). Theory of fun for game design (2nd ed). O'Reilly Media.

Küçükayd?n, M. A., Çite, H., & Ulum, H. (2024). Modelling the relationships between STEM learning attitude, computational thinking, and 21st century skills in primary school. Education and Information Technologies, 29(13), 16641-16659. https://doi.org/10.1007/s10639-024-12492-7

La Paglia, F., La Cascia, C., Francomano, M. M., & La Barbera, D. (2017). Educational robotics to improve mathematical and metacognitive skills. Annual Review of CyberTherapy and Telemedicine, 15(14), 70–75. https://hdl.handle.net/10447/262628

Lampropoulos, G. (2025). Combining artificial intelligence with augmented reality and virtual reality in education: Current trends and future perspectives. Multimodal Technologies and Interaction, 9(2), 11. https://doi.org/10.3390/mti9020011

Leatherdale, S. T. (2019). Natural experiment methodology for research: A review of how different methods can support real-world research. International Journal of Social Research Methodology, 22(1), 19-35. https://doi.org/10.1080/13645579.2018.1488449

Leavy, A., Dick, L., Meletiou-Mavrotheris, M., Paparistodemou, E., & Stylianou, E. (2023). The prevalence and use of emerging technologies in STEAM education: A systematic review of the literature. Journal of Computer Assisted Learning, 39(4), 1061–1082. https://doi.org/10.1111/jcal.12806

Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860-876. https://doi.org/10.1007/s10956-016-9628-2

Liu, W.S., & Wu, T.T. (2024). A study on elementary school students in the middle grades using mBot robot cars explore local communities in an interdisciplinary curriculum. In Y. P. Cheng, M. Pedaste, E. Bardone, & Y. M. Huang (Eds.), Innovative Technologies and Learning. ICITL 2024. (Lecture Notes in Computer Science, Vol. 14786, pp. 33–43). Springer. https://doi.org/10.1007/978-3-031-65884-6_4

Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002

MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1(4), 173–181. https://doi.org/10.1023/A:1026595011371

Mason, R., & Cooper, G. (2013, January). Distractions in programming environments. In Proceedings of the Fifteenth Australasian Computing Education Conference (Vol. 136, pp. 23-30). Australian Computer Society.

Menegatti, E., & Moro, M. (2010, November). Educational robotics from high school to Master of Science. In Workshop Proceedings of Intl. Conf. on Simulation, Modeling and Programming for Autonomous Robots (SIMPAR 2010) (pp. 639-648). Springer.

Merkouris, A., & Chorianopoulos, K. (2015, November). Introducing computer programming to children through robotic and wearable devices. In Proceedings of the workshop in primary and secondary computing education (pp. 69-72). https://doi.org/10.1145/2818314.2818342

Mesurado, B., Vidal, E. M., & Mestre, A. L. (2018). Negative emotions and behaviour: The role of regulatory emotional self-efficacy. Journal of Adolescence, 64, 62-71.

Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR). (2015). Piano Nazionale Scuola Digitale. https://www.miur.gov.it/scuola-digitale

Mokhtar, N., Xuan, L. Z., Lokman, H. F., & Mat, N. H. C. (2023). Theory, literature review, and fun learning method effectiveness in teaching and learning. International Journal of Social Science and Education Research Studies, 3(8), 1738-1744. https://doi.org/10.55677/ijssers/V03I8Y2023-30

Morris, B. J., Owens, W., Ellenbogen, K., Erduran, S., & Dunlosky, J. (2019). Measuring informal STEM learning supports across contexts and time. International Journal of STEM Education, 6, 40. https://doi.org/10.1186/s40594-019-0195-y

Muñoz-Losa, J., & Marcos-Merino, J. M. (2024). Positive emotions and self-efficacy in interactive STEM practices for pre-service teachers. Education Sciences, 14(3), 256. https://doi.org/10.3390/educsci14030256

Murphy, S., MacDonald, A., Wang, C. A., & Danaia, L. (2019). Towards an understanding of STEM engagement: A review of the literature on motivation and academic emotions. Canadian Journal of Science, Mathematics and Technology Education, 19(3), 304-320. https://doi.org/10.1007/s42330-019-00054-w

Musa, A. K., Meshak, B., & Sagir, J. I. (2016). Adolescents' Perception of the Psychological Security of School Environment, Emotional Development and Academic Performance in Secondary Schools in Gombe Metropolis. Journal of Education and Training Studies, 4(9), 144-153.

Nag, S., Katz, J. G., & Saenz-Otero, A. (2013). Collaborative gaming and competition for CS-STEM education using SPHERES Zero Robotics. Acta Astronautica, 83, 145–174. https://doi.org/10.1016/j.actaastro.2012.09.006

Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133-144. https://doi.org/10.1177/1477878509104318

Ouyang, F., & Xu, W. (2024). The effects of educational robotics in STEM education: A multilevel meta-analysis. International Journal of STEM Education, 11(1), 7. https://doi.org/10.1186/s40594-024-00469-4

Özmutlu, M., Atay, D., & Erdo?an, B. (2021). Collaboration and engagement-based coding training to enhance children’s computational thinking self-efficacy. Thinking Skills and Creativity, 40, 100833. https://doi.org/10.1016/j.tsc.2021.100833

Papert, S. (1980). Personal computing and its impact on education. In R. Taylor (Ed.), The computer in the school: Tutor, tool, tutee (pp. 197–202). Teachers College Press.

Pedersen, B., Larsen, J. C., & Nielsen, J. (2020). The effect of commercially available educational robotics: A systematic review. In M. Merdan, W. Lepuschitz, G. Koppensteiner, R. Balogh, & D. Obdržálek (Eds.), Robotics in Education - Current Research and Innovations (pp. 14-27). Springer. https://doi.org/10.1007/978-3-030-26945-6_2

Pekrun, R. (2024). Control-value theory: From achievement emotion to a general theory of human emotions. Educational Psychology Review, 36(3), 83. https://doi.org/10.1007/s10648-024-09909-7

Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). Springer US. https://doi.org/10.1007/978-1-4614-2018-7_12

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Positive emotions in education. In E. Frydenberg (Ed.), Beyond coping: Meeting goals, visions, and challenges (pp. 149–173). Oxford University Press. https://doi.org/10.1093/med:psych/9780198508144.003.0008

Pelgrims, I., Devleesschauwer, B., Vandevijvere, S., De Clercq, E. M., Vansteelandt, S., Gorasso, V., & Van der Heyden, J. (2023). Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian Health Interview Survey. BMC Medical Research Methodology, 23(1), 69. https://doi.org/10.1186/s12874-023-01892-x

Pivetti, M., Di Battista, S., Agatolio, F., Simaku, B., Moro, M., & Menegatti, E. (2020). Educational robotics for children with neurodevelopmental disorders: A systematic review. Heliyon, 6(10), Article e05160 https://doi.org/10.1016/j.heliyon.2020.e05160

Rocha, H. J. B., de Barros Costa, E., & Pimentel, B. A. (2025). An Experimental Study on the Association Between Affective States and Novice Programmers’ Performance. IEEE Transactions on Education. https://doi.org/10.1109/TE.2025.3614598

Roller, S. A., Lampley, S. A., Dillihunt, M. L., Benfield, M. P. J., Gholston, S. E., Turner, M. W., & Davis, A. M. (2020). Development and initial validation of the Student Interest and Choice in STEM (SIC-STEM) Survey 2.0 instrument for assessment of the social cognitive career theory constructs. Journal of Science Education and Technology, 29(5), 646–657. https://doi.org/10.1007/s10956-020-09843-7

Ronsivalle, G. B., Boldi, A., Gusella, V., Inama, C., & Carta, S. (2019). How to implement educational robotics programs in Italian schools: A brief guideline according to an instructional design point of view. Technology, Knowledge and Learning, 24(2), 227–245. https://doi.org/10.1007/s10758-018-9356-9

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860

Scaradozzi, D., Cesaretti, L., Screpanti, L., & Mangina, E. (2020). Identification of the students’ learning process during educational robotics activities. Frontiers in Robotics and AI, 7, 21. https://doi.org/10.3389/frobt.2020.00021

Scaradozzi, D., Screpanti, L., & Cesaretti, L. (2019). Towards a definition of educational robotics: A classification of tools, experiences and assessments. In D. Scaradozzi, L. Guasti, M. Di Stasio, B. Miotti, & E. Blikstein (Eds.), Smart learning with educational robotics: Using robots to scaffold learning outcomes (pp. 63–92). Springer. https://doi.org/10.1007/978-3-030-19913-5_3

Scaradozzi, D., Sorbi, L., Pedale, A., Valzano, M., & Vergine, C. (2015). Teaching robotics at the primary school: an innovative approach. Procedia-Social and Behavioral Sciences, 174, 3838-3846. https://doi.org/10.1016/j.sbspro.2015.01.1122

Shang, X., Jiang, Z., Chiang, F. K., Zhang, Y., & Zhu, D. (2023). Effects of robotics STEM camps on rural elementary students’ self-efficacy and computational thinking. Educational Technology Research and Development, 71(3), 1135-1160. https://doi.org/10.1007/s11423-023-10191-7

Stoffová, V., Zboran, M., & Hyksová, H. (2021). Application of simulation tools in educational robotics. In EDULEARN21 Proceedings (pp. 343–350). IATED. https://doi.org/10.21125/edulearn.2021.0073

Sun, L., Guo, Z., & Hu, L. (2023). Educational games promote the development of students’ computational thinking: A meta-analytic review. Interactive Learning Environments, 31(6), 3476–3490. https://doi.org/10.1080/10494820.2021.1931891

Sun, D., Zhan, Y., Wan, Z. H., Yang, Y., & Looi, C. K. (2025). Identifying the roles of technology: A systematic review of STEM education in primary and secondary schools from 2015 to 2023. Research in Science & Technological Education, 43(1), 145–169. https://doi.org/10.1080/02635143.2023.2251902

Tan, J., Mao, J., Jiang, Y., & Gao, M. (2021). The influence of academic emotions on learning effects: A systematic review. International Journal of Environmental Research and Public Health, 18(18), 9678. https://doi.org/10.3390/ijerph18189678

Tang, H., Qian, Y., & Porter-Voss, S. (2024). Enhancing rural students’ computer science self-efficacy in a robotics-based language arts course. Education and Information Technologies, 29(18), 25533-25550. https://doi.org/10.1007/s10639-024-12875-w

Tekb?y?k, A., Baran Bulut, D. E. M. E. T., & Sandalc?, Y. (2022). Effects of a summer robotics camp on students’ STEM career interest and knowledge structure. Journal of Pedagogical Research, 6(2), 1-18. http://doi.org/10.33902/jpr.202212606

Tisza, G., & Markopoulos, P. (2021). Understanding the role of fun in learning to code. International Journal of Child-Computer Interaction, 28, 100270. https://doi.org/10.1016/j.ijcci.2021.100270

Tsai, M. J., Wang, C. Y., Wu, A. H., & Hsiao, C. Y. (2021). The development and validation of the Robotics Learning Self-Efficacy Scale (RLSES). Journal of Educational Computing Research, 59(6), 1056-1074. https://doi.org/10.1177/0735633121992594

Tze, V., Parker, P., & Sukovieff, A. (2022). Control-value theory of achievement emotions and its relevance to school psychology. Canadian Journal of School Psychology, 37(1), 23-39. https://doi.org/10.1177/08295735211053962

Ulum, H. (2022). A meta-analysis of the effects of different integrated STEM (science, technology, engineering, and mathematics) approaches on primary students' attitudes. International Journal of Educational Research Review, 7(4), 307-317. https://doi.org/10.24331/ijere.1166620

Xia, L., & Zhong, B. (2018). A systematic review on teaching and learning robotics content knowledge in K-12. Computers & Education, 127, 267-282. https://doi.org/10.1016/j.compedu.2018.09.007

Xie, J., Cho, K. W., Wei, T., Xu, J., & Fan, M. (2025). The effects of academic emotions on learning outcomes: A three-level meta-analysis of research conducted between 2000 and 2024. Learning and Motivation, 90, 102109. https://doi.org/10.1016/j.lmot.2025.102109

Yang, F. C. O., Lai, H. M., & Wang, Y. W. (2023). Effect of augmented reality-based virtual educational robotics on programming students’ enjoyment of learning, computational thinking skills, and academic achievement. Computers & Education, 195, 104721. https://doi.org/10.1016/j.compedu.2022.104721

Yan??, H., & Yürük, N. (2020). 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

Yim, I. H. Y., Su, J., & Wegerif, R. (2025). STEAM in practice and research in primary schools: A systematic literature review. Research in Science & Technological Education, 43(4), 1065–1089. https://doi.org/10.1080/02635143.2024.2440424

Zeidner, M. (2014). Anxiety in education. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 265-288). Routledge.

Zhang, J., & Yan, Y. (2025). The power of grit and enjoyment on engagement of students majoring in teaching Chinese to speakers of other languages (TCSOL) in robot-assisted language learning environment: A self-determination theory perspective. Learning and Motivation, 92, 102185. https://doi.org/10.1016/j.lmot.2025.102185

Zhang, Z. (2025). The role of robot-assisted learning in fostering learners’ motivation, self-efficacy, and autonomy: Self-determination theory framework. Learning and Motivation, 92, 102184. https://doi.org/10.1016/j.lmot.2025.102184

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

Downloads

Published

2026-09-30

How to Cite

Pivetti, M. ., Clemente, D. ., Russo, C. ., Dusi, V., & Romano, L. (2026). Comparing LEGO Spike and mBot Robot: Preliminary Data on Math and Science Enjoyment and Self-Efficacy among Lower Secondary School Students. International Journal of Learning, Teaching and Educational Research, 25(9), 371–399. Retrieved from https://www.ijlter.myres.net/index.php/ijlter/article/view/3051