Abstract
One of the aspects of programming that novices often struggle with is the understanding of abstract concepts, such as variables, loops, expressions, and especially Boolean operations. This paper aims to explore the effects of programming tools with different degrees of embodiment on learning Boolean operations in elementary school. To this end, 67 fifth graders were divided into two groups and participated in a 16-week quasi-experiment. The two groups were randomly assigned to two treatments: the Middle Degree of Embodiment class using AS-Block and the High Degree of Embodiment class using Boson Kits. The results indicated that (a) there were no significant differences in learning attitude (p>.05), learning immersion (p>.05), compatibility (p>.05) and cognitive load (p>.05) between the two groups; and (b) the High Degree of Embodiment class performed significantly better in terms of the quality of programming works (p<.01, rG=.533) and the final test score (p<.05, rG=.860) than the Middle Degree of Embodiment class. The experimental results are presented, and their implications for the instruction and development of programming education and embodied learning are addressed.
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Anderson, R. C. (2018). Creative engagement: Embodied metaphor, the affective brain, and meaningful learning. Mind, Brain, and Education, 12(2), 72–81.
Barsalou, L. W. (2010). Grounded cognition: Past, present, and future. Topics in Cognitive Science, 2(4), 716–724.
Bishop-Clark, C., Courte, J., & Howard, E. V. (2006). Programming in pairs with Alice to improve confidence, enjoyment, and achievement. Journal of Educational Computing Research, 34(2), 213–228.
Castro-Alonso, J. C., Ayres, P., & Paas, F. (2015). Animations showing Lego manipulative tasks: Three potential moderators of effectiveness. Computers & Education, 85, 1–13.
Chao, K. J., Huang, H. W., Fang, W. C., & Chen, N. S. (2013). Embodied play to learn: Exploring Kinect-facilitated memory performance. British Journal of Educational Technology, 44(5), 151–155.
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.
Conley, Q., Atkinson, R. K., Nguyen, F., & Nelson, B. C. (2020). MantarayAR: Leveraging augmented reality to teach probability and sampling. Computers & Education, 153, 103895.
Flowerday, T., Schraw, G., & Stevens, J. (2004). The role of choice and interest in reader engagement. Journal of Experimental Education, 72(2), 93–114.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305-321). New York: Springer.
Freudenberg, S., Romero, P., & Du Boulay, B. (2007). "Talking the talk": Is intermediate-level conversation the key to the pair programming success story?. In Agile 2007 (AGILE 2007) (pp. 84-91). IEEE.
Grover, S., & Basu, S. (2017). Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and Boolean logic. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (pp. 267-272). doi: 10.1145/3017680.3017723.
Herman, G. L., Loui, M. C., Kaczmarczyk, L., & Zilles, C. (2012). Describing the what and why of students’ difficulties in Boolean logic. ACM Transactions on Computing Education (TOCE), 12(1), 1–28.
Hermans, F. (2020). Hedy: A gradual language for programming education. In Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 259-270). doi: 10.1145/3372782.3406262.
Hung, I. C., Kinshuk, & Chen, N. S. (2018). Embodied interactive video lectures for improving learning comprehension and retention. Computers & Education, 117, 116–131.
Ioannou, M., & Ioannou, A. (2020). Technology-enhanced Embodied Learning. Educational Technology & Society, 23(3), 81–94.
Johnson-Glenberg, M. C., Birchfield, D. A., Tolentino, L., & Koziupa, T. (2014). Collaborative embodied learning in mixed reality motion-capture environments: Two science studies. Journal of Educational Psychology, 106(1), 86–104.
Johnson-Glenberg, M. C., Megowan-Romanowicz, C., Birchfield, D. A., & Savio-Ramos, C. (2016). Effects of embodied learning and digital platform on the retention of physics content: Centripetal force. Frontiers in Psychology, 7, 1819.
Kallia, M., & Sentance, S. (2020). Threshold concepts, conceptions and skills: teachers' experiences with students' engagement in functions. Journal of Computer Assisted Learning, 37(2), 411–428.
Kaufman, D. B., Felder, R. M., & Fuller, H. (1999). Peer ratings in cooperative learning teams. In Proceedings of the 1999 American Society for Engineering Education (pp. 1-11). Charlotte, NC.
Klahr, D., & Carver, S. M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20(3), 362–404.
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 1–15.
Merkouris, A., & Chorianopoulos, K. (2019). Programming embodied interactions with a remotely controlled educational robot. ACM Transactions on Computing Education (TOCE), 19(4), 1–19.
Munro, M. (2018). Principles for embodied learning approaches. SATJ. South African Theatre Journal, 31(1), 5–14.
Nemirovsky, R., & Ferrara, F. (2009). Mathematical imagination and embodied cognition. Educational Studies in Mathematics, 70(2), 159–174.
Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: a cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434.
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.
Papert, S. (1972). On making a theorem for a child. In Proceedings of the ACM Annual Conference-Volume 1 (pp. 345-349). https://dl.acm.org/doi/pdf/10.1145/800193.569942. Accessed 30 Dec 2021
Piaget, J. (1952). The origins of intelligence in children. (M. Cook, Trans.). W W Norton & Co. doi: 10.1037/11494-000.
Portelance, D. J., & Bers, M. U. (2015). Code and Tell: Assessing young children's learning of computational thinking using peer video interviews with ScratchJr. In Proceedings of the 14th international conference on interaction design and children (pp. 271-274). doi: 10.1145/2771839.2771894.
Post, L. S., Van Gog, T., Paas, F., & Zwaan, R. A. (2013). Effects of simultaneously observing and making gestures while studying grammar animations on cognitive load and learning. Computers in Human Behavior, 29(4), 1450–1455.
Pulvermüller, F. (2005). Brain mechanisms linking language and action. Nature Reviews Neuroscience, 6(7), 576–582.
Shannon, C. E. (1949). The synthesis of two-terminal switching circuits. The Bell System Technical Journal, 28(1), 59–98.
Sun, L., & Zhou, D. (2019). Research status and action path on international children programming education. Open Education Research, 25(2), 23–35.
Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204.
Suzuki, H., & Kata, H. (1995). Interaction-level support for collaborative learning: AlgoBlock—an open programming language. In Proceedings of the first international conference on Computer support for collaborative learning (CSCL 1995) (pp.349-355), Berlin, Germany: Springer Veflag.
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.
Tscholl, M., & Lindgren, R. (2016). Designing for learning conversations: How parents support children's science Learning within an immersive simulation. Science Education, 100(5), 877–902.
Venn, J. (1880). I. On the diagrammatic and mechanical representation of propositions and reasonings. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 10(59), 1–18.
Weng, J. F., Tseng, S. S., & Lee, T. J. (2010). Teaching Boolean logic through game rule tuning. IEEE Transactions on Learning Technologies, 3(4), 319–328.
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636.
Zhong, B., & Li, T. (2020). Can pair learning improve students’ troubleshooting performance in robotics education? Journal of Educational Computing Research, 58(1), 220–248.
Zhong, B., & Wang J. (2021a). Exploring the non-significant difference on students' cognitive load imposed by robotics tasks in pair learning. International Journal of Social Robotics, (3), 1–11.
Zhong B., & Wang Y. (2021b). Effects of roles assignment and learning styles on pair learning in robotics education. International Journal of Technology and Design Education, 31(1), 41–59.
Zhong B., Wang Q., & Chen J. (2016). The impact of social factors on pair programming in a primary school. Computers in Human Behavior, 64, 423–431.
Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An Exploration of Three-Dimensional Integrated Assessment for Computational Thinking. Journal of Educational Computing Research, 53(4), 562–590. https://doi.org/10.1177/0735633115608444.
Zhong, B., Wang, Q., Chen, J., & Li, Y. (2017). Investigating the period of switching roles in pair programming in a primary school. Journal of Educational Technology & Society, 20(3), 220–233.
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Major fostering project of philosophy and social science in South China Normal University "Research on educational models to cultivate students' interdisciplinary creativity" (ZDPY2104).
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Appendix
Appendix
1.1 Appendix 1. Learning attitude survey
Note: Participants were asked to rate themselves with response options ranging from 1 (strongly disagree) to 5 (strongly agree). 1-5 questions are self-confidence dimension, 6-10 questions are enjoyment dimension, and 11-14 questions are value dimension.
Question | Score | ||||
---|---|---|---|---|---|
1. I'm confident of learning robotics | 1 | 2 | 3 | 4 | 5 |
2. I'm sure that I can learn robotics well | 1 | 2 | 3 | 4 | 5 |
3. I have strong self-confidence in learning robotics | 1 | 2 | 3 | 4 | 5 |
4. I'm not good at learning robotics | 1 | 2 | 3 | 4 | 5 |
5. I'm not the type who can learn robotics well | 1 | 2 | 3 | 4 | 5 |
6. I enjoy learning robotics | 1 | 2 | 3 | 4 | 5 |
7. Learning robotics is enjoyable and excited | 1 | 2 | 3 | 4 | 5 |
8. Once I started learning robotics, it was hard to stop | 1 | 2 | 3 | 4 | 5 |
9. The problems and challenges encountered in learning robotics are not attractive to me | 1 | 2 | 3 | 4 | 5 |
10. Learning robotics is boring | 1 | 2 | 3 | 4 | 5 |
11. Learning robotics can improve my computer skills | 1 | 2 | 3 | 4 | 5 |
12. Learning robotics is helpful for learning knowledge of other disciplines | 1 | 2 | 3 | 4 | 5 |
13. Learning robotics makes me more logical and organized | 1 | 2 | 3 | 4 | 5 |
14. Learning robotics is contribute to solving problems in daily life | 1 | 2 | 3 | 4 | 5 |
1.2 Appendix 2. Compatibility survey
Note: Participants were asked to rate themselves with response options ranging from 1 (strongly disagree) to 5 (strongly agree).
Question | Score | ||||
---|---|---|---|---|---|
1. My partner and I cooperate with each other tacitly | 1 | 2 | 3 | 4 | 5 |
2. My partner tried his/her best to do what he/she could do in the process of cooperating to complete the task | 1 | 2 | 3 | 4 | 5 |
3. My partner and I have a lot of communication in the process of completing the task | 1 | 2 | 3 | 4 | 5 |
1.3 Appendix 3. Learning immersion survey
Note: Participants were asked to rate themselves with response options ranging from 1 (strongly disagree) to 5 (strongly agree). 1-5 questions are behavioral engagement dimension, 6-9 questions are emotional engagement dimension, and 10-13 questions are cognitive engagement dimension.
Question | Score | ||||
---|---|---|---|---|---|
1. I can abide by the rules of the robotics classroom | 1 | 2 | 3 | 4 | 5 |
2. I can concentrate for a long time in class | 1 | 2 | 3 | 4 | 5 |
3. I often ask questions in class | 1 | 2 | 3 | 4 | 5 |
4. I can finish the task on time | 1 | 2 | 3 | 4 | 5 |
5. I would repeatedly check the works and constantly improve them | 1 | 2 | 3 | 4 | 5 |
6. I like taking robotics class | 1 | 2 | 3 | 4 | 5 |
7. I feel enjoyable in robotics course | 1 | 2 | 3 | 4 | 5 |
8. I'm interested at the tasks in robotics course | 1 | 2 | 3 | 4 | 5 |
9. I am satisfied with my work | 1 | 2 | 3 | 4 | 5 |
10. I would share what I have learned with those who are not familiar with robotics courses | 1 | 2 | 3 | 4 | 5 |
11. I try to get information related to this course through other resources such as Internet, magazines, books and so on | 1 | 2 | 3 | 4 | 5 |
12. While reading the course textbook, I will think actively to ensure that I can understand the textbook | 1 | 2 | 3 | 4 | 5 |
13. I would read additional materials to learn a concept while learning robotics course | 1 | 2 | 3 | 4 | 5 |
1.4 Appendix 4. Cognitive load survey
In order to complete the task project in the robotics course, if you use a number between 1 and 9 to indicate your level of effort, you will choose:
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Zhong, B., Xia, L. & Su, S. Effects of programming tools with different degrees of embodiment on learning Boolean operations. Educ Inf Technol 27, 6211–6231 (2022). https://doi.org/10.1007/s10639-021-10884-7
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DOI: https://doi.org/10.1007/s10639-021-10884-7