Assessing Computational Thinking,Motivation,and Grit of Undergraduate Students Using Educational Robots

Abstract

Educational technologists and practitioners have made substantial strides in developing affordable digital and tangible resources to support both formal and informal computer science instruction. However, there is a lack of research on practice-based assignments, such as Internet of Things (IoT) projects, that allow undergraduate students to design and demonstrate educational robots using digital or physical assistance, especially when it comes to computational thinking (CT) and programming skills development in association with their psycho-emotional experience. This study compares the impact of Scratch and LEGO^® WeDo robotic kits on students' CT and programming skills development. A quasi-experimental approach was conducted, involving two hundred forty-six participants (n = 246), who were equally divided between Scratch and LEGO^® WeDo groups. Results indicate that the LEGO^® WeDo group showed greater improvement in CT and programming skills development, while designing and presenting IoT projects. Nevertheless, no significant association between motivation, grit, and CT skills was observed. The findings highlight the potential of tangible robotics in facilitating students’ hands-on learning and enhancing motivation to foster CT and programming skills. This study provides a wide range of implications for instructional designers on how to use tangible robotics to support hands-on IoT projects in computer science courses.

Keywords

computational thinking programming IoT scratch LEGO® WeDo undergraduate students quasi-experimental study

Get full access to this article

View all access options for this article.

References

Adanır

G. A.

Gulbahar

(2023). Research trends in K-5 computational thinking education: A bibliometric analysis and ideas to move forward. Education and Information Technologies, 75(2), 661–670. https://doi.org/10.1007/s10639-023-11974-4

Angeli

(2022). The effects of scaffolded programming scripts on pre-service teachers’ computational thinking: Developing algorithmic thinking through programming robots. International Journal of Child-Computer Interaction, 31(6), 100329. https://doi.org/10.1016/j.ijcci.2021.100329

Atmatzidou

Demetriadis

(2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75(2), 661–670. https://doi.org/10.1016/j.robot.2015.10.008

Bati

(2022). A systematic literature review regarding computational thinking and programming in early childhood education. Education and Information Technologies, 27(2), 2059–2082. https://doi.org/10.1007/s10639-021-10700-2

Boz

Allexsaht-Snider

(2022). How do elementary school teachers learn coding and robotics? A case study of mediations and conflicts. Education and Information Technologies, 27(3), 3935–3963. https://doi.org/10.1007/s10639-021-10736-4

Brislin

R. W.

(1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301

Canbeldek

Isikoglu

(2023). Exploring the effects of “productive children: Coding and robotics education program” in early childhood education. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11315-x

Chao

P. Y.

(2016). Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education, 95(2), 202–215.

Chen

Shen

Barth-Cohen

Jiang

Huang

Eltoukhy

(2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers and Education, 109(7), 162–175. https://doi.org/10.1016/j.compedu.2017.03.001

10.

Chiang

Y. H. V.

Cheng

Y. W.

Chen

N. S.

(2023). Improving language learning activity design through identifying learning difficulties in a platform using educational robots and IoT-based tangible objects. Educational Technology and Society, 26(3), 84–100. https://doi.org/10.30191/ETS.202307_26(3).0007

11.

Clark

K. N.

Malecki

C. K.

(2019). Academic Grit Scale: Psychometric properties and associations with achievement and life satisfaction. Journal of School Psychology, 72(S2), 49–66. https://doi.org/10.1016/j.jsp.2018.12.001

12.

Cohen

Manion

Morrison

(2007). Research methods in education. Routledge.

13.

Coşkun

T. K.

Filiz

(2023). The impact of twenty-first century skills on university students’ robotic achievements. Education and Information Technologies, 1–29. https://doi.org/10.1007/s10639-023-11850-1

14.

Duckworth

A. L.

Peterson

Matthews

M. D.

Kelly

D. R.

(2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087

15.

El-Hamamsy

Chessel-Lazzarotto

Bruno

Roy

Cahlikova

Chevalier

Parriaux

Pellet

J.-P.

Lanarès

Zufferey

J. D.

Mondada

(2021). A computer science and robotics integration model for primary school: Evaluation of a large-scale in-service K-4 teacher-training program. Education and Information Technologies, 26(3), 2445–2475. https://doi.org/10.1007/s10639-020-10355-5

16.

Fletcher

Bond

Qualter

(2023). User perspectives of robotic telepresence technology in schools: A systematic literature review. Educational Psychology in Practice, 39(2), 1–18. https://doi.org/10.1080/02667363.2022.2155932

17.

Gao

Zhang

Kahn

Winters

(2022). Grit and learning: Understanding the sequential learning behaviours in snap. Journal of Computer Assisted Learning. 39(2), 603–616. https://doi.org/10.1111/jcal.12766

18.

Gonzalez

(2023). Learning robot differential movements using a new educational robotics simulation tool. Education and Information Technologies. 28(1)1–24. https://doi.org/10.1007/s10639-022-11433-6

19.

Hurt

Greenwald

Allan

Cannady

M. A.

Krakowski

Brodsky

Collins

M. A.

Montgomery

Dorph

(2023). The computational thinking for science (CT-S) framework: Operationalizing CT-S for K–12 science education researchers and educators. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-022-00391-7

20.

Ioannou

Makridou

(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

21.

Kardaş

Eşkisu

Çam

Taytaş

(2022). Development and validation of the grit scale: Test of measurement ınvariance across university and high school students. International Journal of Psychology and Educational Studies, 9(3), 571–588. https://doi.org/10.52380/ijpes.2022.9.3.626

22.

Kellett

Nind

(2014). Ethics in quasi-experimental research on people with severe learning disabilities: Dilemmas and compromises In ethics and research in inclusive education (pp. 168–175). Routledge.

23.

Kerimbayev

Nurym

Akramova

Abdykarimova

(2023). Educational Robotics: Development of computational thinking in collaborative online learning. Education and Information Technologies, 1–23. https://doi.org/10.1007/s10639-023-11806-5

24.

Liu

Zhi

Hicks

Barnes

(2017). Understanding problem solving behavior of 6–8 graders in a debugging game. Computer Science Education, 27, 1–29. https://doi.org/10.1080/08993408.2017.1308651

25.

Mahatanankoon

Sikolia

D. W.

(2017). Intention to remain in a computing program: Exploring the role of passion and grit. In the 23rd Americans conference on information Systems (AMCIS). (pp. 1–10). ACM.

26.

Mahatanankoon

Wolf

(2021). Cognitive learning strategies in an introductory computer programming course. Information Systems Education Journal, 19(3), 11–20.

27.

Mallik

Liu

Kapila

(2023). Analyzing the outcomes of a robotics workshop on the self-efficacy, familiarity, and content knowledge of participants and examining their designs for end-of-year robotics contests. Education and Information Technologies, 28(6), 7225–7264. https://doi.org/10.1007/s10639-022-11400-1

28.

Pellas

Tzafilkou

(2023). The influence of absorption and need for cognition on students’ learning outcomes in educational robot-supported projects. Education Sciences, 13(4), 379. https://doi.org/10.3390/educsci13040379

29.

Pellas

Vosinakis

(2018). The effect of computer simulation games on learning introductory programming: A comparative study on high school students' learning performance by assessing computational problem-solving strategies. Education and Information Technologies, 23(6), 2423–2452. https://doi.org/10.1007/s10639-018-9724-4

30.

Pintrich

P. R.

Smith

D. A. F.

Garcia

Mckeachie

W. J.

(1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (mslq). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024

31.

Sapounidis

Tselegkaridis

Stamovlasis

(2022). Educational robotics and STEM in primary education: A review and a meta-analysis. Journal of Research on Technology in Education, 1–15. https://doi.org/10.1080/15391523.2022.2160394

32.

Schina

Esteve-González

Usart

(2021). An overview of teacher training programs in educational robotics: Characteristics, best practices and recommendations. Education and Information Technologies, 26(3), 2831–2852. https://doi.org/10.1007/s10639-020-10377-z

33.

H.-J.

Kim

Ryoo

(2020). Trajectories of developing computational thinking competencies: Case portraits of Korean gifted girls. The Asia-Pacific Education Researcher, 29(1), 85–100. https://doi.org/10.1007/s40299-019-00459-z

34.

Zhong

Chen

(2023). Technology education in early childhood education: A systematic review. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2022.2160470

35.

Tikva

Tambouris

(2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers and Education, 162(1), 104083. https://doi.org/10.1016/j.compedu.2020.104083

36.

Vaske

J. J.

Beaman

Sponarski

C. C.

(2017). Rethinking internal consistency in cronbach’s alpha. Leisure Sciences, 39(2), 163–173. https://doi.org/10.1080/01490400.2015.1127189

37.

Warner

L. R.

(2008). A best practice guide to intersectional approaches in psychological research. Sex Roles, 59(5–6), 454–463. https://doi.org/10.1007/s11199-008-9504-5

38.

White

M. G.

(2020). Why human subjects research protection is important. The Ochsner Journal, 20(1), 16–33. https://doi.org/10.31486/toj.20.5012

39.

Wing

J. M.

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

40.

Witherspoon

E. B.

Higashi

R. M.

Schunn

C. D.

Baehr

E. C.

Shoop

(2018). Developing computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education, 18(1), 1–20. https://doi.org/10.1145/3104982

41.

Wolf

Jia

(2015). The role of grit in predicting student performance in introductory programming courses: An exploratory study. Proceedings of the southern association for information Systems conference. Hilton Head Island. Paper 21.

42.

Zhan

Zhong

Shi

Zheng

(2022). The design and application of IRobotQ3D for simulating robotics experiments in K‐12 education. Computer Applications in Engineering Education, 30(2), 532–549. https://doi.org/10.1002/cae.22471

43.

Zhong

Zheng

Zhan

(2023). An exploration of combining virtual and physical robots in robotics education. Interactive Learning Environments, 31(1), 370–382. https://doi.org/10.1080/10494820.2020.1786409