Abstract
The study aims to compare the effect of a structured versus an unstructured educational robotics (ER) curriculum on (a) the frequency and type of programming errors made by students in block-based programming, (b) their ability to debug a programme, and (c) their engagement in the learning process. The authors’ hypothesis is that, in programming contexts with young learners, an unstructured ER curriculum might be more beneficial in learning how to debug. This study follows a quasi-experimental design with two comparison groups (n = 35)—a structured ER curriculum group and an unstructured one. Within the quasi-experiment, both qualitative and quantitative data are collected. Findings reveal a list of errors commonly made by both groups. The unstructured ER curriculum group is associated with a significantly higher frequency of errors. The structured ER curriculum group demonstrates significantly greater efficiency in debugging. Yet, the students in the unstructured ER curriculum group outperform their peers in terms of engagement levels.
Similar content being viewed by others
References
Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63–71.
Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954. https://doi.org/10.1016/j.chb.2019.03.018
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), 2. https://doi.org/10.7771/2157-9288.1223
Atmatzidou, S., Demetriadis, S., & Nika, P. (2018). How does the degree of guidance support students’ metacognitive and problem solving skills in educational robotics? Journal of Science Education and Technology, 27(1), 70–85. https://doi.org/10.1007/s10956-017-9709-x
Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers and Education, 58(3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada (Vol. 1, p. 25).
Castledine, A. R., & Chalmers, C. (2011). LEGO Robotics: An authentic problem solving tool? Design and Technology Education: An International Journal, 16(3). Retrieved from https://ojs.lboro.ac.uk/date/
Chiu, C., & Huang, H. (2015). Guided debugging practices of game based programming for novice programmers. International Journal of Information and Education Technology, 5(5), 343–347. https://doi.org/10.7763/ijiet.2015.v5.527
Darabi, A., Arrington, T. L., & Sayilir, E. (2018). Learning from failure: A meta-analysis of the empirical studies. Educational Technology Research and Development, 66(5), 1101–1118. https://doi.org/10.1007/s11423-018-9579-9
DeLiema, D., Dahn, M., Flood, V. J., Asuncion, A., Abrahamson, D., Enyedy, N., & Steen, F. (2019). Debugging as a context for fostering reflection on critical thinking and emotion. Deeper learning dialogic learning and critical thinking: Research-based strategies for the classroom. Hrsg. von Emmanuel Manalo (pp. 209–228). Routledge.
Di Lieto, M. C., Inguaggiato, E., Castro, E., Cecchi, F., Cioni, G., Dell’Omo, M., Laschi, C., Pecini, C., Santerini, G., Sgandurra, G., & Dario, P. (2017). Educational Robotics intervention on Executive Functions in preschool children: A pilot study. Computers in Human Behavior, 71, 16–23. https://doi.org/10.1016/j.chb.2017.01.018
Fields, D. A., Kafai, Y. B., Morales-Navarro, L., & Walker, J. T. (2021). Debugging by design: A constructionist approach to high school students’ crafting and coding of electronic textiles as failure artefacts. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13079
Fitzgerald, S., McCauley, R., Hanks, B., Murphy, L., Simon, B., & Zander, C. (2010). Debugging from the student perspective. IEEE Transactions on Education, 53(3), 390–396. https://doi.org/10.1109/TE.2009.2025266
Gyebi, E., Hanheide, M., & Cielniak, G. (2016). The effectiveness of integrating educational robotic activities into higher education computer science curricula: A case study in a developing country. In D. Alimisis, M. Moro, & E. Menegatti (Eds.), Educational robotics in the makers era. Edurobotics 2016. Advances in intelligent systems and computing. (Vol. 560). Cham: Springer.
Hristova, M., Misra, A., Rutter, M., & Mercuri, R. (2003). Identifying and correcting Java programming errors for introductory computer science students. ACM SIGCSE Bulletin, 35(1), 153–156. https://doi.org/10.1145/792548.611956
Ioannou, A., & Makridou, E. (2018). Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23(6), 2531–2544. https://doi.org/10.1007/s10639-018-9729-z
Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/BF02300500
Kapur, M. (2008). Productive Failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669
Kapur, M. (2011). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science, 39(4), 561–579. https://doi.org/10.1007/s11251-010-9144-3
Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Kapur, M., & Lee, J. (2009). Designing for productive failure in mathematical problem solving. In N. Taatgen & V. R. Hedderick (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2632–2637). Cognitive Science Society.
Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers & Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005
Kim, C. M., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46(5), 767–787. https://doi.org/10.1007/s11251-018-9453-5
Lee, K. T. H., Sullivan, A., & Bers, M. U. (2013). Collaboration by Design: Using Robotics to Foster Social Interaction in Kindergarten. Computers in the Schools, 30(3), 271–281. https://doi.org/10.1080/07380569.2013.805676
Liu, Z., Zhi, R., Hicks, A., & Barnes, T. (2017). Understanding problem solving behavior of 6–8 graders in a debugging game. Computer Science Education, 27(1), 1–29. https://doi.org/10.1080/08993408.2017.1308651
McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: A review of the literature from an educational perspective. Computer Science Education, 18(2), 67–92. https://doi.org/10.1080/08993400802114581
Mitnik, R., Recabarren, M., Nussbaum, M., & Soto, A. (2009). Collaborative robotic instruction: A graph teaching experience. Computers & Education, 53(2), 330–342. https://doi.org/10.1016/j.compedu.2009.02.010
Papert, S. (1980). Mindstorms: Children Computers and Powerful ideas. Basic Books Inc.
Ruiz-del-Solar, J., & Avilés, R. (2004). Robotics courses for children as a motivation tool: The Chilean experience. IEEE Transactions on Education, 47(4), 474–480. https://doi.org/10.1109/TE.2004.825063
Sinha, T., & Kapur, M. (2019). When productive failure fails. Proceedings of the Annual Meeting of the Cognitive Science Society, 41, 2811–2817.
Socratous, C., & Ioannou, A. (2018). A study of collaborative knowledge construction in STEM via educational robotics. In J. Kay & R. Luckin (Eds.), Rethinking learning in the digital age: Making the learning sciences count, 13th International Conference of the Learning Sciences (ICLS) 2018 (Vol. 1, pp. 496–503). London, UK: ISLS. https://doi.org/10.22318/cscl2018.496
Socratous, C., & Ioannou, A. (2019). An empirical study of educational robotics as tools for group metacognition and collaborative knowledge construction. In K. Lund, G. P. Niccolai, E. Lavoué, C. Hmelo-Silver, G. Gweon, & M. Baker (Eds.), A wide lens: Combining embodied, enactive, extended, and embedded learning in collaborative settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) (Vol. 2). Lyon, France: International Society of the Learning Sciences. https://doi.org/10.22318/cscl2019.192
Tobias, S., & Duffy, T. M. (Eds.). (2009). Constructivist instruction: Success or failure. Routledge.
Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. https://doi.org/10.1016/j.learninstruc.2016.01.008
Wu, H. K., & Huang, Y. L. (2007). Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education, 91(5), 727–749. https://doi.org/10.1002/sce.20216
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interests.
Research involving human participants
This study has approval from the local agency for research in schools http://www.pi.ac.cy/pi/index.php?option=com_content&view=article&id=179&Itemid=355&lang=en
Informed consent
Parental consents have been signed.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Following the introductory sessions 1 and 2, in session 3, students had to programme a robot to move accurately along a 1.50 m path, using rotations, degrees, or seconds. The structured curriculum group had to complete a table in their worksheet with various measurements. Then, the students had to programme the robot to follow the path, which was the session’s challenge.
In session 4, the structured curriculum group used a worksheet designed to help them practice different turns and understand how the turning variable is related to distance. Students had to execute several programmes associated with turns and explore the output of their applications. The goal of the challenge was to programme the robot to move in a square without a gyro sensor.
In session 5, students were further exposed to turn-related activities. The worksheet for the structured curriculum was a combination of the worksheets from the previous two sessions focusing on distance and turns. The new challenge was around how to execute different kinds of turns, such as spin turns, pivot turns, and smooth turns. The goal was to programme the robot to move on a path with different turns and arrive at the final destination.
In session 6, students were tasked with using a robot motor with a cargo delivery attachment to move objects. The worksheet for the structured curriculum had two sub-tasks of increasing difficulty that served as introductory activities for the session’s final task. The first sub-task instructed students to programme the robot to move a block that was across from the starting position and the second sub-task instructed students to move a block that was located randomly on the mat.
During sessions 7 and 8, students investigated the colour sensor and the concepts of loops and wait/until. In the structured curriculum, students were asked to place the colour sensor close to several classroom objects and observe the reading value through the programming interface. The purpose was to programme a robot to move along a mat delineated by red and black lines: “When the robot sees a red line, it stops for 1 s and says red, then continues until it finds a black line, stops at the black line and says black, then goes back and forth until it finds the black line ten times”. The challenge for session 8 was to programme an autonomous robot that could move along a desk without falling off for one minute.
In session 9, students had to programme the robot to stop at an object once it was reached. The worksheet for the structured curriculum asked the students to place the robot across from several objects in the classroom and measure the distance between the robot and the object using an ultrasonic sensor. This session’s challenge was to programme a robot that could move around the classroom without hitting any objects.
In session 10, students used worksheets to explore the concept of conditional logic. They were asked to programme the robot to say “red” when the colour sensor detected the colour red and “no” when the colour sensor detected any other colour. This activity was designed to help students with the final task, i.e., completing the line-following challenge (see Fig.
3).
Rights and permissions
About this article
Cite this article
Socratous, C., Ioannou, A. Structured or unstructured educational robotics curriculum? A study of debugging in block-based programming. Education Tech Research Dev 69, 3081–3100 (2021). https://doi.org/10.1007/s11423-021-10056-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11423-021-10056-x