Utilizing botley robotics to facilitate the development of computational thinking skills in children with visual impairment
Cucuk Wawan Budiyanto, Universitas Sebelas Maret, Indonesia
Suharno Suharno, Universitas Sebelas Maret, Indonesia
Abstract
Computational thinking (CT) skills have become increasingly vital in the digital age, particularly for children with visual impairments who often encounter challenges in accessing technological education. This study aims to explore the use of Botley Robotics as a tool to facilitate CT skills and its application in the learning context. Employing qualitative methods through observations, questionnaires, and interviews, this research involved two students with visual impairments from a special needs school in Indonesia, selected through purposive sampling. Botley, a screenless robot, served as the primary learning medium. The data collected were analyzed using the CT framework from Brennan and Resnick, which encompasses three main dimensions: computational concepts, practices, and perspectives. The findings indicate that Botley Robotics effectively facilitates CT skills in students, particularly in areas such as debugging, programming, and logical reasoning. Despite their visual limitations, the students demonstrated the ability to program the robot and understand complex statements and logical operators. The conclusions drawn from this research suggest that Botley can serve as a valuable tool for fostering CT skills among students with visual impairments by integrating concepts from the Brennan & Resnick framework. The tactile and auditory feedback provided by Botley enables children to develop problem-solving and logical thinking skills through direct interaction. This study highlights the significance of incorporating robotic technology into inclusive education and demonstrates the substantial potential of Botley Robotics to enhance access to and the quality of education for children with visual impairments. Therefore, it is recommended that this technology be implemented more broadly within the context of inclusive education.
Keywords
References
Ahn, J. H., Sung, W., Lee, S. W., & Hee, J. (2017, March). COBRIX: A physical computing interface for blind and visually impaired students to learn programming. In Society for Information Technology & Teacher Education international conference (pp. 727-733). Association for the Advancement of Computing in Education (AACE).
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. doi: 10.1016/j.robot.2015.10.008.
Ary, D., Jacobs, L. C., & Razavieh, A. (2014). Introduction to research in education edition. Belmont, CA: Wadsworth Cengage Learning.
Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., . . . Malagoli, C. 2022. Reviewing Computational Thinking in Compulsory Education: State of Play and Practices from Computing Education.
Brady, T., Salas, C., Nuriddin, A., Rodgers, W., & Subramaniam, M. 2014. MakeAbility: Creating Accessible Makerspace Events in a Public Library. Public Library Quarterly.
Brennan, K., & Resnick, M. 2012. New Frameworks for Studying and Assessing the Development of Computational Thinking. Paper Presented at the Proceedings of the 2012 Annual Meeting of the American Educational Research Association. Vancouver, Canada.
Budiyanto, C., Fitriyaningsih, R. N., Kamal, F., Ariyuana, R., &. Efendi. 2020. “Hands-on Learning in STEM: Revisiting Educational Robotics as a Learning Style Precursor. Open Engineering. 10(1):649-657.
Chibaudel, Q., Johal, W., Oriola, B., JM Macé, M., Dillenbourg, P., Tartas, V., & Jouffrais, C. (2020, October). " If you've gone straight, now, you must turn left"-Exploring the use of a tangible interface in a collaborative treasure hunt for people with visual impairments. In Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-10). doi: 10.1145/3373625.3417020.
Costa, R., Araújo, C., & Henriques, P. R. (2021). Melodic-Teaching computational thinking to visually impaired kids. In Second International Computer Programming Education Conference (ICPEC 2021). Schloss-Dagstuhl-Leibniz Zentrum für Informatik.
Creswell, John W., and J. Creswell, David. 2017. Research Design Qualitative, Quantitative, and Mixed Methods. 5th ed. SAGE Publications.
Dagienė,, V., & Stupuriene, G. (2016). Bebras--A Sustainable Community Building Model for the Concept Based Learning of Informatics and Computational Thinking. Informatics in education, 15(1), 25-44.
Doyle, L., McCabe, C., Keogh, B., Brady, A., & McCann, M. (2020). “An Overview of the Qualitative Descriptive Design within Nursing Research.” Journal of Research in Nursing, 25(5):443-455.
Dulock, H. L. 1993. “Research Design: Descriptive Research.” Journal of Pediatric Oncology Nursing 10(4):154–57.
Eppich, W. J., Gormley, G. J., & Teunissen, P. W. 2019. “In-Depth Interviews. Healthcare Simulation Research: A Practical Guide.” 85-91.
Erwin, E. J., T. S. Perkins, J. Ayala, M. Fine, and E. Rubin. 2001. “‘You Don’t Have to Be Sighted to Be a Scientist, Do You?’ Issues and Outcomes in Science Education.” Journal of Visual Impairment and Blindness 95(6):338–52. doi: 10.1177/0145482x0109500603.
Fagerlund, J., Vesisenaho, M., & Häkkinen, P. 2022. “Fourth-Grade Students’ Computational Thinking in Pair Programming with Scratch: A Holistic Case Analysis.” International Journal of Child-Computer Interaction, (100511):33.
Grover, S., & Pea, R. 2018. “Computational Thinking: A Competency Whose Time Has Come. Computer Science Education: Perspectives on Teaching and Learning in School.” 19.
Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., & Lim, H. (2021). An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking. Computers in Human Behavior, 114, 106575. doi: 10.1016/j.chb.2020.106575.
Kock, E. d. 2018. “Botley Robot Teaches Coding without Screens - The Coding Robot Activity Set Review.” Retrieved (https://www.techagekids.com/2018/05/botley-robot-teaches-coding-no-screens.html).
Maheshwari, S. K., Chaturvedi, R., & Sharma, P. (2021). Effectiveness of psycho-educational intervention on psychological distress and self-esteem among resident elderly: A study from old age homes of Punjab, India. Clinical Epidemiology and Global Health, 11, 100733.. doi: 10.1016/j.cegh.2021.100733.
Metatla, O., Bardot, S., Cullen, C., Serrano, M., & Jouffrais, C. (2020, April). Robots for inclusive play: Co-designing an educational game with visually impaired and sighted children. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-13).. doi: 10.1145/3313831.3376270.
Mich, O., Ghislandi, P. M. M., Massa, P., Mardare, V., Bisutti, T., & Giacomozzi, D. 2021. “A Framework for Educational Robotics in Kindergarten: A Systematic Literature Review and Analysis. International Journal of Digital Literacy and Digital Competence (IJDLDC).” 12(2):22–53.
Miles, M. B., & Huberman, A. M. (1984). Qualitative data analysis. Beverly Hills.
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of Computational Thinking, Digital Competence and 21st Century Skills When Learning Programming in K-9. Education Inquiry. 11(1):1–17.
Park, Y., & Shin, Y. 2019. “Comparing the Effectiveness of Scratch and App Inventor about Learning Computational Thinking Concepts. Electronics.” 8(11):1269.
Perry, N. E. 2023. Using Qualitative Methods to Enrich Understandings of Self-Regulated Learning. In Using Qualitative Methods To Enrich Understandings of Self-Regulated Learning. Routledge.
Ragusa, G., & Leung, L. 2023. “The Impact of Early Robotics Education on Students’ Understanding of Coding, Robotics Design, and Interest in Computing Careers. Sensors.” 23(23):9335.
Shute, V. J., Sun, C., & Asbell-Clarke, J. 2017. “Demystifying Computational Thinking. Educational Research Review.” 22:142-158.
Taherdoost, H. 2016. “Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research.” SSRN Electronic Journal. doi: 10.2139/ssrn.3205035.
Warmbrod, J. R. 2014. “Reporting and Interpreting Scores Derived from Likert-Type Scales.” Journal of Agricultural Education 55(5):30-47.
Yadav, A., & Berges, M. (2019). Computer science pedagogical content knowledge: Characterizing teacher performance. ACM Transactions on Computing Education (TOCE), 19(3), 1-24. doi: 10.1145/3303770.
Zhang, L., & Nouri, J. 2019. “A Systematic Review of Learning Computational Thinking through Scratch in K-9. Computers & Education.” (141):103607.
DOI: https://doi.org/10.21831/jptk.v30i2.74680
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Mey Tias Andry Pamungkas, Cucuk Wawan Budiyanto, Suharno Suharno
This work is licensed under a Creative Commons Attribution 4.0 International License.
Online (e-ISSN): 2477-2410 || Printed (p-ISSN): 0854-4735
Jurnal Pendidikan Teknologi Kejuruan by http://journal.uny.ac.id/index.php/jptk was distributed under a Creative Commons Attribution 4.0 International License.
View My StatsSupported by:
Social Media: