Research Trends in Robotics-Based Learning and Students’ Conceptual Understanding in STEM Education: A Bibliometric and Systematic Review
Keywords:
Bibliometric Analysis, Conceptual Understanding, Educational Robotics, Robotics-Based Learning, STEM EducationAbstract
Educational robotics is increasingly used to support active and integrated STEM learning, yet the extent to which it develops students’ conceptual understanding remains unclear. This study maps the development of robotics-based STEM learning research and examines how conceptual understanding has been assessed and reported. An integrated bibliometric and systematic-review design was applied to Scopus documents published from 2001 to 2025. Of 59 retrieved records, eight false positives were excluded, resulting in a bibliometric corpus of 51 documents and an abstract-stage empirical evidence subset of 34 studies. Performance indicators, contributors, geographic collaboration, normalized author-keyword co-occurrence, educational characteristics, conceptual-evidence strength, and direction of findings were analyzed. The corpus received 693 citations and achieved an h-index of 13; 56.9% of the publications appeared during 2023–2025. The United States, Taiwan, and China led publication output, while international collaboration remained limited. Four keyword clusters represented the core educational-robotics field, digital and computational tools, constructionist pedagogy, and an emerging artificial-intelligence and equity frontier. Within the empirical subset, computing or robotics concepts accounted for 50.0% of studies, and the robot most often functioned as a construction or programming object. Only ten studies provided direct conceptual evidence, compared with 19 proximal and five indirect studies. Although 24 abstracts reported positive findings, these direction counts are not equivalent to pooled causal effects. The field is expanding rapidly, but its conceptual evidence remains methodologically uneven and concentrated in broad knowledge-performance outcomes. Full-text screening, quality appraisal, and construct-specific effect synthesis are therefore required.
References
Akgün, M., & Atıcı, B. (2023). Bibliometric map of educational robotics studies. International Journal of Technology in Education, 6(2), 283–309. https://doi.org/10.46328/ijte.402
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), 2324487. https://doi.org/10.1080/2331186X.2024.2324487
Aleksandrova, N. S., Sokolova, E. G., Kosarenko, N. N., Ibragimova, E. M., Budkevich, R. L., & Yagudina, R. I. (2024). Exploring the frontiers: A comprehensive bibliometric analysis of robotics in science education. Eurasia Journal of Mathematics, Science and Technology Education, 20(10), em2520. https://doi.org/10.29333/ejmste/15432
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), Article 2. https://doi.org/10.7771/2157-9288.1223
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008
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
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
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
Chen, L., Cai, Y., Fang, Y., Yang, Z., Xia, D., You, J., Xiao, S., Song, Y., Zhan, L., Chen, J., & Sun, L. (2025). Observing a robot peer’s failures facilitates students’ classroom learning. Science Robotics, 10(106), eadu5257. https://doi.org/10.1126/scirobotics.adu5257
Chiu, M.-C., Hwang, G.-J., & Tu, Y.-F. (2024). Roles, applications, and research designs of robots in science education: A systematic review and bibliometric analysis of journal publications from 1996 to 2020. Interactive Learning Environments, 32(5), 1834–1859. https://doi.org/10.1080/10494820.2022.2129392
Chou, P.-N. (2018). Skill development and knowledge acquisition cultivated by maker education: Evidence from Arduino-based educational robotics. Eurasia Journal of Mathematics, Science and Technology Education, 14(10), 1–15. https://doi.org/10.29333/ejmste/93483
Darmawansah, D., Hwang, G.-J., Chen, M.-R. A., & Liang, J.-C. (2023). Trends and research foci of robotics-based STEM education: A systematic review from diverse angles based on the technology-based learning model. International Journal of STEM Education, 10(1), 12. https://doi.org/10.1186/s40594-023-00400-3
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A. A., Zinonos, Z., & Chatzichristofis, S. A. (2020). Educational robotics: Platforms, competitions and expected learning outcomes. IEEE Access, 8, 219534–219562. https://doi.org/10.1109/ACCESS.2020.3042555
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., & Vedel, I. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
Hsiao, H.-S., Chen, J.-H., Chang, T.-L., Li, P.-H., & Chung, G.-H. (2024). A study on the effects of using the 6E model and a robot teaching assistant on junior high school students’ STEM knowledge, learning motivation, and hands-on performance. Journal of Science Education and Technology, 33(5), 759–778. https://doi.org/10.1007/s10956-024-10119-7
Hu, C.-C., Yang, Y.-F., Cheng, Y.-W., & Chen, N.-S. (2024). Integrating educational robot and low-cost self-made toys to enhance STEM learning performance for primary school students. Behaviour and Information Technology, 43(8), 1614–1635. https://doi.org/10.1080/0144929X.2023.2222308
Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3, 11. https://doi.org/10.1186/s40594-016-0046-z
López-Belmonte, J., Segura-Robles, A., Moreno-Guerrero, A.-J., & Parra-González, M.-E. (2021). Robotics in education: A scientific mapping of the literature in Web of Science. Electronics, 10(3), 291. https://doi.org/10.3390/electronics10030291
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/BM.2012.031
Merkouris, A., Chorianopoulou, B., Chorianopoulos, K., & Chrissikopoulos, V. (2019). Understanding the notion of friction through gestural interaction with a remotely controlled robot. Journal of Science Education and Technology, 28(3), 209–221. https://doi.org/10.1007/s10956-018-9760-2
Nagai, K. (2001). Learning while doing: Practical robotics education. IEEE Robotics and Automation Magazine, 8(2), 39–43. https://doi.org/10.1109/100.932756
Nugent, G., Barker, B., Grandgenett, N., & Welch, G. (2016). Robotics camps, clubs, and competitions: Results from a US robotics project. Robotics and Autonomous Systems, 75, 686–691. https://doi.org/10.1016/j.robot.2015.07.011
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
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195. https://doi.org/10.1016/j.joi.2016.10.006
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: An extension to the PRISMA Statement for reporting literature searches in systematic reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z
Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3–20. https://doi.org/10.1007/s10798-015-9304-5
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
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
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
