Indonesian Undergraduate Students’ Perception of Their Computational Thinking Ability
AbstractBecome skilled at CT is indispensable for undergraduate students, as the proficiency in information technologies and complex problem solving increase in important in digital workplaces. This study measured Indonesian undergraduate students' self-perception of their CT ability in order to establish CT profile based on gender, majors of specialization, and university location. Study participant comprises of 527 final-year undergraduate students from three universities in Indonesia, using the Hi-ACT instrument. To examine whether statistically significant differences existed, independent sample t-test was used. The findings regarding the profile of Indonesian undergraduates’ CT skill show, the students attained a moderately high CT level. In particular, statistically significant differences existed in Problem Solving and Communication between male and female students, wherein male students means were higher. Regarding majors of specialization, significant differences between STEM and non-STEM students were found in Algorithmic Thinking, Decomposition, Evaluation, Generalization, and Communication, in favor of STEM students. As for university location, significant differences were found in Algorithmic Thinking, Debugging, Teamwork, and Communication, in which suburban students performed better.
Guzdial, M., 2008, Education paving the way for computational thinking, Commun. ACM, vol. 51, no. 8, p. 25.
Czerkawski, B. and Lyman III, E., 2015, Exploring issues about computational thinking in higher education, TechTrends, vol. 59, no. 2, pp. 57–65.
Swaid, S. I., 2015, Bringing computational thinking to STEM education, Procedia Manuf., vol. 3, no. 2015, pp. 3657–3662.
Mason, D., Khan, I., and Farafontov, V., 2016, Computational thinking as a liberal study, Proceedings of the 47th ACM Technical Symposium on Computing Science Education, pp. 24–29.
Kafura, D., Bart, A. C., and Chowdhury, B., 2015, Design and preliminary results from a computational thinking course, Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education, pp. 63–68.
Rubinstein, A., and Chor, B., 2014, Computational thinking in Life Science education, PLoS Comput. Biol., vol. 10, no. 11, pp. 1–6.
Miller, L. D. et al., 2013, Improving learning of computational thinking using creative thinking exercises in CS-1 computer science courses, 2013 IEEE Frontiers in Education Conference, pp. 1426–1432.
Boechler, P., Artym, C., Dejong, E., Carbonaro, M., and Stroulia, E., 2014, Computational thinking, code complexity, and prior experience in a videogame-building assignment, 2014 IEEE 14th International Conference on Advanced Learning Technologies, pp. 396–398.
Walden, J., Doyle, M., Garns, R., and Hart, Z., 2013, An informatics perspective on computational thinking, Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education., pp. 4–9.
Gouws, L., Bradshaw, K., and Wentworth, P., 2013, First year student performance in a test for computational thinking, Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, pp. 271–277.
Csernoch, M., Biró, P., Máth, J., and Abari, K., 2015, Testing algorithmic skills in traditional and non-traditional programming environments, Informatics Educ., vol. 14, no. 2, pp. 175–197.
Korkmaz, Ö., Cakir, R., and Ozden, M. Y., 2017, A validity and reliability study of the computational thinking scales (CTS), Comput. Human Behav., vol. 72, pp. 558–569.
Wing, J. M., 2006, Computational thinking, Commun. ACM, vol. 49, no. 3, pp. 33–35.
Aristawati, F. A., Budiyanto, C., and Yuana, R. A., 2018, Adopting educational robotics to enhance undergraduate students’ self-efficacy levels of computational thinking, J. Turkish Sci. Educ., vol. 15, pp. 42–50.
Anistyasari, Y., Ekohariadi, and Kurniawan, A., 2018, Exploring computational thinking to improve energy-efficient programming skills, MATEC Web of Conferences, pp. 4–7.
Fakhriyah, F., Masfuah, S., and Mardapi, D., 2019, Developing scientific literacy-based teaching materials to improve students’ computational thinking skills, J. Pendidik. IPA Indones. [Indonesian J. Sci. Educ., vol. 8, no. 4, pp. 482–491.
Bebras Indonesia, Workshop – Situs Resmi Bebras Indonesia, http://bebras.or.id/v3/workshop/, Accessed: 29-Nov-2017.
Sax, L. J. et al., 2017, Anatomy of an enduring gender gap: the evolution of women’s participation in Computer Science, 2017, J. Higher Educ., vol. 88, no. 2, pp. 258–293.
Beyer, S., 2014, Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades, Comput. Sci. Educ., vol. 24, no. 2–3, pp. 153–192.
Durak, Y. H. and Saritepeci, M., 2018, Analysis of the relation between computational thinking skills and various variables with the structural equation model, Comput. Educ., vol. 116, no. 2018, pp. 191–202.
Gunbatar, M. S. and Karalar, H., 2018, Gender differences in middle school students’ attitudes and self-efficacy perseptions towards mBlock programming, Eur. J. Educ. Res., vol. 7, no. 4, pp. 925–933.
Sullivan, K., Byrne, J. R., Bresnihan, N., O’Sullivan, K. and B. Tangney, 2015, CodePlus - Designing an after school computing programme for girls, 2015 IEEE Frontiers in Education Conference, 2015, vol. 2014, pp. 1–5.
Park, C. J., Hyun, J. S. and Heuilan, J., 2015, Effects of gender and abstract thinking factors on adolescents’ computer program learning, Proceedings - Frontiers in Education Conference.
Román-gonzález, M., Perez-Gonzelez, J. C. and Jimenez-Fernandez, C., 2016, Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test, Comput. Human Behav., vol. 72, no. July 2017, pp. 678–691.
Korkmaz, Ö. and Bai, X., 2019, Adapting computational thinking scale (CTS) for Chinese high school students and their thinking scale skills level, Particip. Educ. Res., vol. 6, no. 1, pp. 10–26.
Atmatzidou, S. and Demetriadis, S., 2016, Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences,” Rob. Auton. Syst., vol. 75, pp. 661–670.
Werner, L., Denner, J., Campe, S. and Kawamoto, D.C., 2012, The fairy performance assessment: Measuring computational thinking in middle school, Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, pp. 215–220.
Jenson, J. and Droumeva, M., 2016, Exploring media literacy and computational thinking: A game maker curriculum study, Electron. J. e-Learning, vol. 14, no. 2, pp. 111–121.
Howell, L., Jamba, L., Kimball, A. S. and Sanchez-Ruiz, A., 2011, Computational thinking: Modeling Applied to the Teaching and Learning of English, Proceedings of the 49th Annual Southeast Regional Conference, p. 48.
Shell, D. F., Hazley, L., Patterson, M., Soh, L. D., Miller, L., Chiriacescu, V. and Ingraham, E., 2014, Improving learning of computational thinking using creative thinking exercises in CS-1 computer science courses, 2014 IEEE Frontiers in Education Conference, pp. 1426–1432.
Yang, H. I. et al., 2011, A novel interdisciplinary course in gerontechnology for disseminating computational thinking, in 2011 Frontiers in Education Conference, pp. 1–6.
PDDikti-Ristekdikti, 2018, Statistik Pendidikan Tinggi [Higher Educational Statistical Year Book] 2018.
Logli, C., 2016, Higher education in Indonesia: contemporary challenges in governance, access, and quality, The Palgrave Handbook of Asia Pacific Higher Education, C. S. Collins, M. N. N. Lee, J. N. Hawkins, and D. E. Neubauer, Eds., pp. 561–581.
Sondakh, D. E., Osman, K. and Zainudin, S., 2020, A Pilot Study of an Instrument to Assess Undergraduates’ Computational thinking Proficiency, Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 11, pp. 263–273.
Kurnianingsih, E. and Retnawati, H., 2019, A needs assessment for development of learning set to improve students’ higher-order thinking skills and self-confidence, Character Education for 21st Century Global Citizens, pp. 415–424.
Hadi, S., Retnawati, H., Munadi, S., Apino, E. and Wulandari, N. F., 2018, The difficulties of high school students in solving higher-order thinking skills problems, Probl. Educ. 21st Century, vol. 76, no. 4.
Young, B. J., 2000, Gender differences in student attitudes toward computers, J. Res. Comput. Educ., vol. 33, no. 2, pp. 204–216.
Kafura, D., Bart, A. C. and Chowdhury, B., 2018, A computational thinking course accessible to non-stem majors, J. Comput. Sci. Coll., vol. 34, no. 2, pp. 157–163.
Bosworth, D., Lyonette, C., Wilson, R., Bayliss, M. and Fathers, S., 2013, The Supply of and Demand for Design Skills.
Jang, H., 2016, Identifying 21st century STEM competencies using workplace data, J. Sci. Educ. Technol., vol. 25, no. 2, pp. 284–301.
Sirakaya, D. A., 2020, Investigation of computational thinking in the context of ICT and mobile technologies, Int. J. Comput. Sci. Educ. Sch., vol. 3, no. 4, pp. 50–59.
Ariansyah, K., Anandhita, V. H. and Sari, D., 2019, Investigating the next level digital divide in Indonesia, The 2019 4th Technology Innovation Management and Engineering Science International Conference, pp. 1–5.
Bell, T. and Lodi, M., 2019, Constructing computational thinking without using computers, Constr. Found., vol. 14, no. 3, pp. 342–351.
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).