Educational Robotics for Advanced-Level Science Teaching in Rwanda:Learner Perceptions, Implementation Barriers, and Pathways for Sustainable Integration
DOI:
https://doi.org/10.58197/rmgqjt64Keywords:
educational robotics, learner perceptions, advanced-level science education, STEM educationAbstract
Educational robotics is increasingly positioned as a practical vehicle for STEM learning, computational thinking, creativity, problem solving, and technology literacy. Yet the effectiveness of robotics depends strongly on context, pedagogy, teacher capacity, infrastructure, and learner access. This study examined learner perceptions of the benefits, constraints, and practical pathways for integrating educational robotics into advanced-level science teaching in Rwanda. A mixed-methods case-study design was used with 88 upper secondary learners in Nyanza District. Questionnaire data were summarized using frequencies and percentages and were complemented by key-informant interviews and documentary analysis. The findings indicate high perceived educational value: 82% of respondents strongly agreed and 16% agreed with a combined statement that robotics could improve learning related performance and practical engagement. Learners also associated robotics with curiosity, innovation, creative work, and better decision making. However, substantial barriers were identified. Eighty-eight percent strongly agreed that robotics is financially demanding; 92% strongly agreed that insufficient instructional materials constrain inclusive implementation; and 95% strongly agreed that robotics raises concerns about future employment displacement. In response, 98% strongly supported emphasizing creativity and innovation, 89% supported strengthening decision making, and 76% strongly supported linking robotics activities to income generating innovation. The study interprets these findings against recent international meta analyses and systematic reviews showing positive average effects of educational robotics on STEM learning while emphasizing the importance of teacher support, instructional design, and access to resources. In the Rwandan context, the results align with national priorities around STEM, digital skills, smart classrooms, competency-based education, and technology enabled learning. The paper concludes that robotics should be introduced through phased, curriculum aligned, teacher supported, and resource sensitive models rather than as a standalone technological novelty. Recommendations are offered for teacher professional development, low-cost implementation, inclusive participation, ethical and future-of-work education, and stronger multi-school research designs.
References
Abbasi, B. N., & Yang, C. (2026). Assessing the impact of digital equity initiatives and government policies on university access, student achievement, and retention in low-income countries. elematics and Informatics, 107, Article 102398.https://doi.org/10.1016/j.tele.2026.102398
Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. ndian journal of medical specialties, 4(2), 330–333.
Alagbonsi, A. I., Gbadamosi, M. A., Nemerimana, M., Olubiyi, M. V., Onaadepo, O., Ime, A. U., Feyitimi, A.-R. A., Mungai, R. W., Rutagarama, F., Zambrano, D. M., Omeiza, N. A., & Elias, S. O. (2026). How effective is online teaching of physiology modules? Perception of Rwandan health sciences students. urrent Research in Physiology, 9, Article 100181.https://doi.org/10.1016/j.crphys.2025.100181
Callari, T. C., Vecellio Segate, R., Hubbard, E.-M., Daly, A., & Lohse, N. (2024). An ethical framework for human-robot collaboration for the future people-centric manufacturing: A collaborative endeavour with European subject-matter experts in ethics. echnology in Society, 78, Article 102680.https://doi.org/10.1016/j.techsoc.2024.102680
Chari, W., Mutseekwa, C., & Dzavo, J. (2025). Implementing Rwanda’s Competency-Based Curriculum in Teacher Training Colleges: A Systematic Review Across Policy, Institutional, and Classroom Levels. eacher Education (ISSN 2232-0458/e-ISSN 2550-1771), 15(2), 73–85.
Choudhary, P., & Potdar, P. (2023). Robotics in STEM Education: Enhancing Engagement, Skills, and Future Readiness. nternational Journal of Advanced Research in Science, Communication and Technology, 3(1), 749–759.
Chuene, D. M., & Teane, F. M. (2024). Resource inadequacy as a barrier to effective curriculum implementation by life sciences teachers in South Africa. outh African Journal of Education, 44(2), 1–10.
Comstock, G. (2013). Research ethics: A philosophical guide to the responsible conduct of research. ambridge University Press.
Godzicki, L., Godzicki, N., Krofel, M., & Michaels, R. (2013). Increasing Motivation and Engagement in Elementary and Middle School Students through Technology-Supported Learning Environments. nline Submission.
Hampden-Thompson, G., & Bennett, J. (2013). Science teaching and learning activities and students' engagement in science. nternational Journal of Science Education, 35(8), 1325–1343.
Hyldahl, K., & Gamlem, S. M. (2026). Through the lens of feedback literacy: A scoping review of empirical research on teacher educators’ feedback practices in initial teacher education. tudies in Educational Evaluation, 90, Article 101646.https://doi.org/10.1016/j.stueduc.2026.101646
Ineza, W. B., Izere, E. C., Sonnenschein, R., Kukunda, C. B., Tumwebaze, F., Shumbusho, R., & Razak, K. A. (2025). Application of geospatial and ICT technologies for landslide disaster risk reduction in Rwanda. rogress in Disaster Science, 28, Article 100466.https://doi.org/10.1016/j.pdisas.2025.100466
Iyamuremye, A., Twagilimana, I., & Niyonzima, F. N. (2024). Examining the utilization of web-based discussion tools in teaching and learning organic chemistry in selected Rwandan secondary schools. eliyon, 10(20), Article e39356.https://doi.org/10.1016/j.heliyon.2024.e39356
Jean Claude, B., Christine, N., & Raymond, N. (2025). Children’s computer use time at school in basic education: Perspectives of teachers and education stakeholders. ocial Sciences & Humanities Open, 12, Article 101986.https://doi.org/10.1016/j.ssaho.2025.101986
Khapre, S., Mersha, M. A., Olalekan, Z., Shakil, H., Iskander, S., Moustafa, A., & Kalita, J. (2026). Agentic AI systems: A systematic survey of multi-agent architectures, cognitive foundations, interaction, explainability, security, and performance evaluation. eurocomputing, 696, Article 134049.https://doi.org/10.1016/j.neucom.2026.134049
Khoso, A. K., & Honggang, W. (2026). Technostress forecasting in EFL classrooms: How digital fatigue and perceived AI ethical concerns shape digital burnout and undermine SDG-4 achievement. cta Psychologica, 269, Article 107396.https://doi.org/10.1016/j.actpsy.2026.107396
Lu, Y., & Geng, S. (2026). The effects of educational robotics on students’ learning outcomes: A three-level meta-analysis. ducational Research Review, 51, Article 100790.https://doi.org/10.1016/j.edurev.2026.100790
Manohar, N., MacMillan, F., Steiner, G. Z., & Arora, A. (2018). Recruitment of research participants. andbook of research methods in health social sciences, 3, 71–98.
Marshall, G. (2005). The purpose, design and administration of a questionnaire for data collection. adiography, 11(2), 131–136.
Marín-Marín, J.-A., García-Tudela, P. A., & Duo-Terrón, P. (2024). Computational thinking and programming with Arduino in education: A systematic review for secondary education. eliyon, 10(8), Article e29177.https://doi.org/10.1016/j.heliyon.2024.e29177
Mohajan, H. K. (2017). Two criteria for good measurements in research: Validity and reliability. nnals of Spiru Haret University. Economic Series, 17(4), 59–82.
Mustafy, T., & Rahman, M. T. U. (2024). SPSS. In Statistics and data analysis for engineers and scientists (pp. 135–180). Springer Nature Singapore.
Mutoni, D., Delespaul, D., Wuyts, C., Draulans, V., & Meeusen, C. (2026). The role of parental and teacher involvement on students’ self-confidence in Science, Technology, Engineering, and Mathematics: Comparing boys and girls in Kayonza (Rwanda) high schools. nternational Journal of Educational Development, 123, Article 103595.https://doi.org/10.1016/j.ijedudev.2026.103595
Myers, J. L., Well, A. D., & Lorch, R. F., Jr. (2013). Research design and statistical analysis. outledge.
Nikolic, N., Djapan, M., Macuzic, I., Savkovic, M., & Vukicevic, A. (2026). Digitalization of industrial safety education and training: A systematic review of progress, challenges, and future directions. esults in Engineering, 32, Article 112679.https://doi.org/10.1016/j.rineng.2026.112679
Norma, J., Eisenschmidt, E., & Kikas, E. (2026). Teachers’ expectancy-value-cost profiles in professional development to support students’ self-regulated learning. eaching and Teacher Education, 176, Article 105518.https://doi.org/10.1016/j.tate.2026.105518
Nsabayezu, E., Habimana, O., Nzabalirwa, W., & Niyonzima, F. N. (2025). Examining students’ engagement and motivation in organic chemistry through the use of a multimedia-supported flipped classroom approach. ducation for Chemical Engineers, 53, 102–112.https://doi.org/10.1016/j.ece.2025.08.001
Omrany, H., Al-Obaidi, K. M., Ghaffarianhoseini, A., Chang, R.-D., Park, C., & Rahimian, F. (2026). Digital twin technology for education, training and learning in construction industry: Implications for research and practice. ngineering, Construction and Architectural Management, 33(3), 1836–1870.https://doi.org/10.1108/ECAM-10-2024-1376
Parmaxi, A., Zaphiris, P., & Ioannou, A. (2016). Enacting artifact-based activities for social technologies in language learning using a design-based research approach. omputers in Human Behavior, 63, 556–567.https://doi.org/10.1016/j.chb.2016.05.072
Qi, L., Xu, X., Wu, Y., Wang, F., & Wei, W. (2026). Investigating AI competence among Chinese secondary school teachers: Insights from the UNESCO framework. cta Psychologica, 267, Article 107053.https://doi.org/10.1016/j.actpsy.2026.107053
Sen, C., Ay, Z. S., & Kiray, S. A. (2021). Computational thinking skills of gifted and talented students in integrated STEM activities based on the engineering design process: The case of robotics and 3D robot modeling. hinking Skills and Creativity, 42, Article 100931.https://doi.org/10.1016/j.tsc.2021.100931
Shu, L., Xie, S., & Lin, H. (2026). Effects of physically embodied educational robots on children’s learning outcomes: A meta-analysis. cta Psychological, 265, Article 106576.https://doi.org/10.1016/j.actpsy.2026.106576
Tamene, E. H. (2016). Theorizing conceptual framework. sian Journal of Educational Research Vol, 4(2), 50–56.
Tsingidou, S., & Sapounidis, T. (2026). Fostering computational thinking through robotics in kindergarten: A systematic review of learning strategies, frameworks, and assessment. educational Research Review, 52, Article 100829.https://doi.org/10.1016/j.edurev.2026.100829
Van Deursen, A. J., Van Der Zeeuw, A., De Boer, P., Jansen, G., & Van Rompay, T. (2021). Digital inequalities in the Internet of Things: differences in attitudes, material access, skills, and usage. information, Communication & Society, 24(2), 258–276
Vu, H. T. T., Phan, H. T., Tran, M. C. T., Le, V. A. T., Ho, N. V., Doan, M. H. T., Nguyen, T. P. T., & Pham, T. H. (2026). Competency-based education policies and assessment reform in early childhood and primary education: International experiences and policy implications for Vietnam. ocial Sciences & Humanities Open, 14, Article 103462.https://doi.org/10.1016/j.ssaho.2026.103462
Walker, J., & Almond, P. (2010). Interpreting statistical findings: a guide for health professionals and students. cGraw-Hill Education.
Wang, Y. (2026). Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills. hinking Skills and Creativity, 60, Article 102068.https://doi.org/10.1016/j.tsc.2025.102068
Wang, Y., Zhang, Z. J., & Zou, D. (2026). Multimodality and social interactions in AI-enhanced embodied robot-assisted language learning: A meta-analysis. ducational Research Review, 52, Article 100833.https://doi.org/10.1016/j.edurev.2026.100833
Wickham, H. (2016). Data analysis. In ggplot2: elegant graphics for data analysis (pp. 189–201). Springer.
Wouters, R., Cox, A., & Raes, A. (2026). Educational artefacts matter. collaborative inquiry into the complexity of classrooms. Learning in Context, 3, Article 100023.https://doi.org/10.1016/j.lecon.2026.100023
Yeung, R. C. Y., Yeung, C. H., Sun, D., Keane, T., & Yang, Y. (2026). From simulation to flight: Simulation-assisted drone learning with teacher-AI co-designed scaffolds for secondary students’ STEM knowledge and competencies. omputers and Education: Artificial Intelligence, 11, Article 100651.https://doi.org/10.1016/j.caeai.2026.100651
Zhang, Z. (2025). The role of robot-assisted learning in fostering learners’ motivation, self-efficacy, and autonomy: Self-determination theory framework. earning and Motivation, 92, Article 102184.https://doi.org/10.1016/j.lmot.2025.102184
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