Learning through computer science unplugged on team assisted individualization on the computational thinking ability

Wahyudin Wahyudin; Amellya Mustikaningtyas Rishanty; Muhammad Nursalman; Shah Nazir; Lala Septem Riza

doi:10.21744/lingcure.v5nS3.1841

Authors

Wahyudin
wahyudin_sanusi@upi.edu
Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
Amellya Mustikaningtyas Rishanty Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
Muhammad Nursalman Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
Shah Nazir Department of Computer Science, University of Swabi, Swabi, Pakistan
Lala Septem Riza Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia

Keywords:

computational thinking skills, computer science, learning, team assisted individualization, unplugged media

Abstract

Technical facilities are an element that can support educational and learning activities, especially for professional students in the fields of technology and information. Computational thinking is also one of the supportive skills that every student must have. This is consistent with the preliminary research conducted by the researchers. The teacher said that when students learned basic programming materials, they found it difficult and slowed down because of the limited media they used and the non-optimal methods. This research is aimed to determine the effect of unplugged media based on team assisted individualization on the computational thinking skills of vocational high school students in basic programming subjects. The method used in this research is Quasi-experiment using pretest and posttest control group design. The results of the survey are as follows. 1) Learning media was declared available by media professionals at a rate of 94.23%, including the "very good" category. (2) The designed learning media can improve students' mathematical thinking skills with an average profit value of 0.49, and the standard of effectiveness is "medium". (3) Student response to the use of learning media shows a 97.87% percentage value in the "Very Good" category.

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References

Altam, S. . (2020). Influence of social media on EFL Yemeni learners in Indian Universities during Covid-19 Pandemic. Linguistics and Culture Review, 4(1), 35-47. https://doi.org/10.21744/lingcure.v4n1.19

Artut, P. D. (2009). Experimental evaluation of the effects of cooperative learning on kindergarten children's mathematics ability. International journal of educational research, 48(6), 370-380. https://doi.org/10.1016/j.ijer.2010.04.001

Artz, D., & Gil, Y. (2007). A survey of trust in computer science and the semantic web. Journal of Web Semantics, 5(2), 58-71. https://doi.org/10.1016/j.websem.2007.03.002

Azzajjad, M. F., Tendrita, M., & Ahmar, D. S. (2021). Effect of animation and review video making (arvima) in non-classical learning model on independent learning and students’ learning outcome. Linguistics and Culture Review, 5(S3), 967-976. https://doi.org/10.21744/lingcure.v5nS3.1657

Bell, T., & Vahrenhold, J. (2018). CS unplugged—how is it used, and does it work?. In Adventures between lower bounds and higher altitudes (pp. 497-521). Springer, Cham.

Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20-29.

Cho, Y., & Lee, Y. (2017). Possibility of Improving Computational Thinking Through Activity Based Learning Strategy for Young Children. Journal of Theoretical & Applied Information Technology, 95(18).

Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191-202. https://doi.org/10.1016/j.compedu.2017.09.004

Fabry, D. L., & Higgs, J. R. (1997). Barriers to the effective use of technology in education: Current status. Journal of educational computing research, 17(4), 385-395.

Fu, Q. K., & Hwang, G. J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119, 129-143. https://doi.org/10.1016/j.compedu.2018.01.004

Hedegaard, M. (2014). The significance of demands and motives across practices in children's learning and development: An analysis of learning in home and school. Learning, culture and social interaction, 3(3), 188-194. https://doi.org/10.1016/j.lcsi.2014.02.008

Kanca, I. N., Ginaya, G., & Sri Astuti, N. N. (2021). Collaborative network learning (CNL) on students’ online learning. International Journal of Linguistics, Literature and Culture, 7(5), 362-370. https://doi.org/10.21744/ijllc.v7n5.1923

Lee, J. (2020). Coding in early childhood. Contemporary Issues in Early Childhood, 21(3), 266-269.

Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction, 13(2), 177-189. https://doi.org/10.1016/S0959-4752(02)00019-1

Morimoto, Y. V., Che, Y. S., Minamino, T., & Namba, K. (2010). Proton-conductivity assay of plugged and unplugged MotA/B proton channel by cytoplasmic pHluorin expressed in Salmonella. FEBS letters, 584(6), 1268-1272. https://doi.org/10.1016/j.febslet.2010.02.051

Nishida, T., Idosaka, Y., Hofuku, Y., Kanemune, S., & Kuno, Y. (2008, July). New methodology of information education with “computer science unplugged”. In International Conference on Informatics in Secondary Schools-Evolution and Perspectives (pp. 241-252). Springer, Berlin, Heidelberg.

Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & education, 52(1), 1-12. https://doi.org/10.1016/j.compedu.2008.06.004

Posamentier, A. S., & Stepelman, J. (1990). Teaching secondary school mathematics (pp. 447-450). New York: Merrill.

Rinartha, K., Suryasa, W., & Kartika, L. G. S. (2018). Comparative Analysis of String Similarity on Dynamic Query Suggestions. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 399-404). IEEE.

Riza, L. S., Aryani, I. A., Wihardi, Y., & Rahman, E. F. (2018). Development of Computational Story for Teaching Algorithm and Programming. In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT) (pp. 326-329). IEEE.

Riza, L. S., Sawiji, T., Nurjanah, N., Haviluddin, H., Budiman, E., & Rosales-Pérez, A. (2020). A Labyrinth Game for Blind Children Using Problem Solving Learning Model.

Sauppé, A., Szafir, D., Huang, C. M., & Mutlu, B. (2015, February). From 9 to 90: Engaging learners of all ages. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 575-580).

Shomirzayev, S. (2021). National followers in the students use of educational technologies instruction of interests. International Journal of Linguistics, Literature and Culture, 7(3), 152-157. https://doi.org/10.21744/ijllc.v7n3.1508

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003

Thomas, V., Azmitia, M., & Whittaker, S. (2016). Unplugged: Exploring the costs and benefits of constant connection. Computers in Human Behavior, 63, 540-548. https://doi.org/10.1016/j.chb.2016.05.078

Wei, X., Lin, L., Meng, N., Tan, W., & Kong, S. C. (2021). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education, 160, 104023. https://doi.org/10.1016/j.compedu.2020.104023

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.