Embodied Learning for Computational Thinking in a Mixed-Reality Context

Abstract

This study examined the effects of embodied learning experiences on students’ understanding of computational thinking (CT) concepts and their ability to solve CT problems. In a mixed-reality learning environment, students mapped CT concepts, such as sequencing and loops, onto their bodily movements. These movements were later applied to robot programming tasks, where students used the same CT concepts in a different modality. By explicitly connecting embodied actions with programming tasks, the intervention aimed to enhance students’ comprehension and transfer of CT skills. Forty-four first- and second-grade students participated in the study. The results showed significant improvements in students’ CT competency and positive attitudes toward CT. Additionally, an analysis of robot programming performance identified common errors and revealed how students employed embodied strategies to overcome challenges. The effects of embodied learning and the impact of embodied learning strategies were discussed.

Keywords

computational thinking embodied learning mixed-reality CT education robot programming K-5 education

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References

Agbo

F. J.

Oyelere

S. S.

Suhonen

Tukiainen

(2023). Design, development, and evaluation of a virtual reality game-based application to support computational thinking. Educational Technology Research & Development, 71(2), 505–537. https://doi.org/10.1007/s11423-022-10161-5

Allen

K. R.

Smith

K. A.

Bird

L.-A.

Tenenbaum

J. B.

Makin

T. R.

Cowie

(2024). Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience. Psychonomic Bulletin & Review, 31(3), 1364–1375. https://doi.org/10.3758/s13423-023-02400-4

Andrews

Halford

G. S.

(2002). A cognitive complexity metric applied to cognitive development. Cognitive Psychology, 45(2), 153–219. https://doi.org/10.1016/S0010-0285(02)00002-6

Angeli

Valanides

(2020). Developing young children's computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, Article 105954. https://doi.org/10.1016/j.chb.2019.03.018

Angeli

Voogt

Fluck

Webb

Cox

Malyn-Smith

Zagami

(2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47–57. https://www-jstor-org-443.web.bisu.edu.cn/stable/jeductechsoci.19.3.47

Barr

Harrison

Conery

(2011, 0/0/). Computational thinking: A digital age skill for everyone. Learning and Leading with Technology, 38(6), 20–23. https://www.citeulike-article-id:10297515

Bers

M. U.

Flannery

Kazakoff

E. R.

Sullivan

(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

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Critten

Hagon

Messer

(2022, 2022/08/01). Can pre-school children learn programming and coding through guided play activities? A case study in computational thinking. Early Childhood Education Journal, 50(6), 969–981. https://doi.org/10.1007/s10643-021-01236-8

10.

Fan

Antle

A. N.

Warren

J. L.

(2020). Augmented reality for early language learning: A systematic review of augmented reality application design, instructional strategies, and evaluation outcomes. Journal of Educational Computing Research, 58(6), 1059–1100. https://doi.org/10.1177/0735633120927489

11.

Fofang

J. B.

Weintrop

Moon

Williams-Pierce

(2021). Computational bodies: Grounding computational thinking practices in embodied gesture. In de Vries

Hod

Ahn

(Eds.), Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021, Bochum, Germany, Jun 8-11, 2021 (pp. 171–178). International Society of the Learning Sciences. https://doi.org/10.22318/icls2021.171

12.

Garzón

Kinshuk Baldiris

Gutiérrez

Pavón

(2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review, 31, Article 100334. https://doi.org/10.1016/j.edurev.2020.100334

13.

Gautam

Williams

Terry

Robinson

Newbill

(2018). Mirror worlds: Examining the affordances of a next generation immersive learning environment. TechTrends, 62(1), 119–125. https://doi.org/10.1007/s11528-017-0233-x

14.

Glenberg

A. M.

(2008). Embodiment for education. In Calvo

Gomila

(Eds.), Handbook of cognitive science (pp. 355–372). Elsevier. https://doi.org/10.1016/B978-0-08-046616-3.00018-9

15.

Grover

Basu

(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, Seattle, Washington. https://doi.org/10.1145/3017680.3017723.

16.

Grover

Pea

(2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189x12463051

17.

Grover

Pea

(2018). Computational thinking: A competency whose time has come. In Sentance

Barendsen

Schulte

(Eds.), Computer science education: Perspectives on teaching and learning in school (Vol. 19, pp. 19–38). Bloomsbury Academic. https://doi.org/10.5040/9781350296947.ch-005

18.

Hald

L. A.

van den Hurk

Bekkering

(2015). Learning verbs more effectively through meaning congruent action animations. Learning and Instruction, 39, 107–122. https://doi.org/10.1016/j.learninstruc.2015.05.010

19.

Ijsselsteijn

Riva

(2003). Being there: The experience of presence in mediated environments. In Riva

Davide

Ijsselsteijn

(Eds.), Being There: Concepts, effects and measurement of user presence in synthetic environments (pp. 1–14). Ios Press.

20.

Johnson-Glenberg

M. C.

(2019). The necessary nine: Design principles for embodied VR and active stem education. In Díaz

Ioannou

Bhagat

K. K.

Spector

J. M.

(Eds.), Learning in a digital world: Perspective on interactive technologies for formal and informal education (pp. 83–112). Springer. https://doi.org/10.1007/978-981-13-8265-9_5

21.

Johnson-Glenberg

M. C.

Megowan-Romanowicz

(2017). Embodied science and mixed reality: How gesture and motion capture affect physics education. Cognitive Research: Principles and Implications, 2(1), 24. https://doi.org/10.1186/s41235-017-0060-9

22.

Keefer

L. A.

Landau

M. J.

Sullivan

Rothschild

Z. K.

(2014). Embodied metaphor and abstract problem solving: Testing a metaphoric fit hypothesis in the health domain. Journal of Experimental Social Psychology, 55, 12–20. https://doi.org/10.1016/j.jesp.2014.05.012

23.

Kim

Tscholl

(2021). Young children’s embodied interactions with a social robot. Educational Technology Research & Development, 69(4), 2059–2081. https://doi.org/10.1007/s11423-021-09978-3

24.

Kopcha

T. J.

Ocak

Qian

(2021). Analyzing children’s computational thinking through embodied interaction with technology: A multimodal perspective. Educational Technology Research & Development, 69(4), 1987–2012. https://doi.org/10.1007/s11423-020-09832-y

25.

Kwon

(2017). Student’s misconception of programming reflected on problem-solving plans. International Journal of Computer Sciences and Engineering Systems, 1(4), 14–24. https://doi.org/10.21585/ijcses.v1i4.19

26.

Kwon

Jeon

Zhou

Kim

Brush

T. A.

(2022). Embodied learning for computational thinking in early primary education. Journal of Research on Technology in Education, 56(4), 410–430. https://doi.org/10.1080/15391523.2022.2158146

27.

Kwon

Kim

Seo

Kim

Brush

(2024). Embodied learning in a mixed-reality environment: Examination of student embodiment. In Blankenship

R. J.

Cherner

(Eds.), Research highlights in technology and teacher education special edition (pp. 155–174). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/224717/

28.

Lee

Martin

Denner

Coulter

Allan

Erickson

Malyn-Smith

Werner

(2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37. https://doi.org/10.1145/1929887.1929902

29.

Lindgren

Morphew

Kang

Junokas

(2019). An embodied cyberlearning platform for gestural interaction with cross-cutting science concepts. Mind, Brain, and Education, 13(1), 53–61. https://doi.org/10.1111/mbe.12191

30.

Macedonia

(2019). Embodied learning: Why at school the mind needs the body. Frontiers in Psychology, 10, 2098. https://doi.org/10.3389/fpsyg.2019.02098

31.

Magana

A. J.

Balachandran

(2017). Students’ development of representational competence through the sense of touch. Journal of Science Education and Technology, 26(3), 332–346. https://doi.org/10.1007/s10956-016-9682-9

32.

Moore

J. W.

Fletcher

P. C.

(2012). Sense of agency in health and disease: A review of cue integration approaches. Consciousness and Cognition, 21(1), 59–68. https://doi.org/10.1016/j.concog.2011.08.010

33.

Müller

Overton

W. F.

Reene

(2001). Development of conditional reasoning: A longitudinal study. Journal of Cognition and Development, 2(1), 27–49. https://doi.org/10.1207/S15327647JCD0201_2

34.

Nathan

M. J.

Walkington

(2017). Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language. Cognitive Research: Principles and Implications, 2(1), 9. https://doi.org/10.1186/s41235-016-0040-5

35.

Odermatt

I. A.

Buetler

K. A.

Wenk

Özen

Ö.

Penalver-Andres

Nef

Mast

F. W.

Marchal-Crespo

(2021). Congruency of information rather than body ownership enhances motor performance in highly embodied virtual reality. Frontiers in Neuroscience, 15, Article 678909. https://doi.org/10.3389/fnins.2021.678909

36.

Ottenbreit-Leftwich

A. T.

Kwon

Brush

T. A.

Karlin

Jeon

Jantaraweragul

Guo

Nadir

Gok

Bhattacharya

(2021). The impact of an issue-centered problem-based learning curriculum on 6th grade girls’ understanding of and interest in computer science. Computers and Education Open, 2, Article 100057. https://doi.org/10.1016/j.caeo.2021.100057

37.

Peppler

Thompson

Danish

Moczek

Corrigan

(2020). Comparing first- and third-person perspectives in early elementary learning of honeybee systems. Instructional Science, 48(3), 291–312. https://doi.org/10.1007/s11251-020-09511-8

38.

Qiu

Liu

Wang

Huang

(2019). Representation of human spatial navigation responding to input spatial information and output navigational strategies: An ALE meta-analysis. Neuroscience & Biobehavioral Reviews, 103, 60–72. https://doi.org/10.1016/j.neubiorev.2019.06.012

39.

Rijke

W. J.

Bollen

Eysink

T. H.

Tolboom

J. L.

(2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in Education, 17(1), 77–92. https://doi.org/10.15388/infedu.2018.05, https://www.ceeol.com/search/article-detail?id=645612

40.

Sahin

Yilmaz

R. M.

(2020). The effect of Augmented Reality Technology on middle school students' achievements and attitudes towards science education. Computers & Education, 144, Article 103710. https://doi.org/10.1016/j.compedu.2019.103710

41.

Samurcay

(1989). The concept of variable in programming: Its meaning and use in problem-solving by novice programmers. In Soloway

Spohrer

J. C.

(Eds.), Studying the novice programmer (pp. 161–178). Lawrence Erlbaum.

42.

Seiter

Foreman

(2013). Modeling the learning progressions of computational thinking of primary grade students. In Proceedings of the ninth annual international ACM conference on International computing education research, San Diego, San California, USA. https://doi.org/10.1145/2493394.2493403

43.

Shute

V. J.

Sun

Asbell-Clarke

(2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003

44.

Siegler

R. S.

(1976). Three aspects of cognitive development. Cognitive Psychology, 8(4), 481–520. https://doi.org/10.1016/0010-0285(76)90016-5

45.

Statter

Armoni

(2020). Teaching abstraction in computer science to 7th grade students. ACM Transction on Computing Education, 20(1), 1–37. https://doi.org/10.1145/3372143

46.

Thees

Kapp

Strzys

M. P.

Beil

Lukowicz

Kuhn

(2020). Effects of augmented reality on learning and cognitive load in university physics laboratory courses. Computers in Human Behavior, 108, Article 106316. https://doi.org/10.1016/j.chb.2020.106316

47.

Van Merriënboer

J. J. G.

Sweller

(2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0

48.

Weisberg

S. M.

Newcombe

N. S.

(2017). Embodied cognition and STEM learning: Overview of a topical collection in CR:PI. Cognitive Research: Principles and Implications, 2(1), 1–6. https://doi.org/10.1186/s41235-017-0071-6

49.

Wing

J. M.

(2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

50.

Wing

J. M.

(2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118

51.

Kang

Yan

(2022). Understanding embodied immersion in technology-enabled embodied learning environments. Journal of Computer Assisted Learning, 38(1), 103–119. https://doi.org/10.1111/jcal.12594

52.

Zeng

Yang

Bautista

(2023). Computational thinking in early childhood education: Reviewing the literature and redeveloping the three-dimensional framework. Educational Research Review, 39, Article 100520. https://doi.org/10.1016/j.edurev.2023.100520

53.

Zhang

Chen

Hwang

G.-J.

Y.-F.

(2023). Engaging young students in effective robotics education: An embodied learning-based computer programming approach. Journal of Educational Computing Research, 62(2), 532–558. https://doi.org/10.1177/07356331231213548

54.

Zhong

Liu

Zhan

(2023). A literature review on the empirical studies of technology-based embodied learning. Interactive Learning Environments, 31(8), 5180–5199. https://doi.org/10.1080/10494820.2021.1999274