The Mediating Role of Computational Identity in the Relationship between Computational Thinking Skills and Academic Self-Efficacy
Keywords:
computational thinking, creative interest, academic self-efficacy, mediation analysis, Ethiopian higher education
Abstract
Computational thinking (CT) has emerged as a critical 21st-century skill, yet its motivational and psychological correlates in higher education remain underexplored, particularly in African contexts. Creative interest (CI) and academic self-efficacy (ASE) represent key motivational mechanisms that may link CT perceptions to broader academic confidence. Purpose: This study examined the direct and indirect relationships among computational thinking, creative interest, and academic self-efficacy among undergraduate students, testing whether creative interest mediates the effect of CT on ASE. Methods: A cross-sectional survey was administered to 357 undergraduate students at Dire Dawa University, Ethiopia. Validated self-report scales measured overall and subscale levels of CT, CI, and ASE. Data were analyzed using descriptive statistics, Pearson correlations, confirmatory factor analysis (CFA), and bootstrapped mediation analysis with PROCESS macro. CFA supported the distinctiveness of the three constructs with acceptable fit (CFI = 0.92, RMSEA = 0.072) and strong validity (AVE > 0.76). CT showed strong positive correlations with CI (r = 0.594) and ASE (r = 0.632). Mediation analysis revealed a significant total effect of CT on ASE (β = 0.356, p < .001), a significant direct effect after mediation (β = 0.289, p < .001), and a small but significant indirect effect via CI (β = 0.067, 95% bootstrap CI [0.033, 0.101]), accounting for 18.8% of the total effect. The model explained 42.5% of variance in ASE. Conclusion: Computational thinking perceptions enhance academic self-efficacy both directly and partially through creative interest, highlighting a motivational pathway in which CT fosters creative identity that, in turn, supports efficacy beliefs. Ethiopian universities should integrate CT training with activities that explicitly link computational skills to creative expression and self-regulated learning to maximize motivational and efficacy benefits.
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References
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Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.
Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, *72*, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
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Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Ramlan, R. Goshu, B.S., (2025), Science Citizens: Bridging Knowledge and Action for All, Budapest International Research and Critics in Linguistics and Education, 8(4), 274-302
Sellami, A., & Elkhoudary, E. A. (2024). Enriching computing identity frameworks: Integrating current constructs and unveiling new dimensions for today's tech-savvy world—a systematic review. Frontiers in Education, *9*, Article 1366906. https://doi.org/10.3389/feduc.2024.1366906
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Tissenbaum, M., Sheldon, J., & Abelson, H. (2019). From computational thinking to computational action. Communications of the ACM, *62*(3), 34–36. https://doi.org/10.1145/3265747
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Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
Wing, J. M. (2006). Computational thinking. Communications of the ACM, *49*(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wu, T.-T., Silitonga, L. M., Dharmawan, B., & Murti, A. T. (2024). Empowering students to thrive: The role of CT and self-efficacy in building academic resilience. Journal of Educational Computing Research, *62*(3), 816–845. https://doi.org/10.1177/07356331241234567
Zapata-Casabon, C. (2025). Predictors of success and retention: The influence of belonging and self-efficacy on first-year engineering students. In 2025 ASEE Annual Conference & Exposition. American Society for Engineering Education. https://nemo.asee.org/public/conferences/365/author_index/136198
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking: Approaches and orientations in K-12 education. In *Proceedings of EdMedia 2016--World Conference on Educational Media and Technology* (pp. 13-18). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/p/172925/
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421
Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, *44*(8), 1187–1218. https://doi.org/10.1002/tea.20237
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355–369. https://doi.org/10.1007/s40692-017-0090-9
Dunning, D. (2011). The Dunning–Kruger effect: On being ignorant of one’s own ignorance. Advances in Experimental Social Psychology, 44, 247–296. https://doi.org/10.1016/B978-0-12-385522-0.00005-6
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, *5*(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, *41*(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications. (No DOI for core textbook edition; see https://doi.org/10.1177/0095798418796892 for related methodological validation).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, *18*(3), 233–239. https://doi.org/10.1111/j.1467-9280.2007.01882.x
Gee, J. P. (2000). Identity as an analytic lens for research in education. Review of Research in Education, 25(1), 99–125. https://doi.org/10.3102/0091732X025001099
Geske, A., Kiseļova, R., & Pole, O. (2025). Achieving excellence in computational thinking: Insights from the ICILS 2023 study. Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference, 3, 135–143. https://doi.org/10.17770/etr2025vol3.8546
Goshu, B.S., (2025), Transforming Science Education in Ethiopian Higher Education: Harnessing Artificial Intelligence for Innovation and Equity, Turkish Journal of Teacher Education, 14(2), 102, 120
Goshu, B.S., M.M. Weldeamueal, Muhammad Ridwan, (2025), Barriers to Excellence: Unveiling the Challenges in Modern Science Education a Case of Ethiopia's Education System, Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 8(1), 26-44.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression based approach (2nd ed.). Guilford Press.
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, *6*(1), 1–55. https://doi.org/10.1080/10705519909540118
Jinks, J., & Morgan, V. (1999). Children's perceived academic self-efficacy: An inventory scale. The Clearing House, *72*(4), 224–230. https://doi.org/10.1080/00098659909599398
Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.
Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, *72*, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
Kuo, Y.-T. (2025). Learning programming: Exploring the relationships of self-efficacy, computational thinking, and learning performance among minority students. Frontiers in Education, 10, Article 1623415. https://doi.org/10.3389/feduc.2025.1623415
Lei, H., Chiu, M. M., Li, F., Wang, X., & Geng, Y. (2020). Computational thinking and academic achievement: A meta-analysis among students. Children and Youth Services Review, *118*, Article 105439. https://doi.org/10.1016/j.childyouth.2020.105439
Liao, C. H. (2022). Exploring the relationship between computational thinking and learning satisfaction for non-STEM college students. International Journal of Educational Technology in Higher Education, 19, Article 46. https://doi.org/10.1186/s41239-022-00347-5
Mahadeo, J., Hazari, Z., & Potvin, G. (2020). Developing a computing identity framework: Understanding computer science and information technology career choice. ACM Transactions on Computing Education, *20*(1), Article 7. https://doi.org/10.1145/3365571
Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, *38*(1), 30–38. https://doi.org/10.1037/0022-0167.38.1.30
Nannim, F. A., Mosia, M., Ezema, M. E., & Egara, F. (2026). Exploring the relationship between computational thinking dimensions and achievement in robotics programming among computer education students. Journal of Technology and Science Education, *16*(1), 95–108. https://doi.org/10.3926/jotse.3402
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Ramlan, R. Goshu, B.S., (2025), Science Citizens: Bridging Knowledge and Action for All, Budapest International Research and Critics in Linguistics and Education, 8(4), 274-302
Sellami, A., & Elkhoudary, E. A. (2024). Enriching computing identity frameworks: Integrating current constructs and unveiling new dimensions for today's tech-savvy world—a systematic review. Frontiers in Education, *9*, Article 1366906. https://doi.org/10.3389/feduc.2024.1366906
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
Stormes, K. N. (2024). Compiling a computing identity: A byte of self-efficacy, belonging, and other predictive factors (Publication No. 979-8-3828-2052-1) [Doctoral dissertation, University of California, Los Angeles]. ProQuest Dissertations & Theses Global.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Tissenbaum, M., Sheldon, J., & Abelson, H. (2019). From computational thinking to computational action. Communications of the ACM, *62*(3), 34–36. https://doi.org/10.1145/3265747
Vuong, M., Brown-Welty, S., & Tracz, S. (2010). The effects of self-efficacy on academic success of first-generation college sophomore students. Journal of College Student Development, *51*(1), 50–64. https://doi.org/10.1353/csd.0.0109
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
Wing, J. M. (2006). Computational thinking. Communications of the ACM, *49*(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wu, T.-T., Silitonga, L. M., Dharmawan, B., & Murti, A. T. (2024). Empowering students to thrive: The role of CT and self-efficacy in building academic resilience. Journal of Educational Computing Research, *62*(3), 816–845. https://doi.org/10.1177/07356331241234567
Zapata-Casabon, C. (2025). Predictors of success and retention: The influence of belonging and self-efficacy on first-year engineering students. In 2025 ASEE Annual Conference & Exposition. American Society for Engineering Education. https://nemo.asee.org/public/conferences/365/author_index/136198
Published
2025-12-15
How to Cite
Belay Sitotaw Goshu, & Muhammad Ridwan. (2025). The Mediating Role of Computational Identity in the Relationship between Computational Thinking Skills and Academic Self-Efficacy. Konfrontasi: Jurnal Kultural, Ekonomi Dan Perubahan Sosial, 12(4), 300-328. https://doi.org/10.33258/konfrontasi2.v12i4.360
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