Skip to main content
SpringerLink
Log in

How do computational thinking self-efficacy and performance differ according to secondary school students’ profiles? The role of computational identity, academic resilience, and gender

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

In recent years, computational thinking (CT) initiatives have been increasing in both research and practice. Although the importance of students’ resilience and computational identity in the CT development process is recognized, more research is needed on their role on students’ CT skills. Therefore, little is known about whether differences in students’ CT performance and CT self-efficacy (CTSE) are related to computational identity and academic resilience for programming (ARP). This study aims to understand how secondary school students’ latent profiles are distributed according to computational identity and ARP using a person-centered approach. Afterward, the current research examines how these profiles differ according to CT test performance and CTSE scores. The participants of the study consisted of 601 secondary school students. Latent profile analysis revealed four profiles based on computational identity and resilience: (a) low, (b) low to moderate, (c) moderate to high, (d) high. The effect of profile membership and gender on CTSE and CT test performance was determined by two-way ANOVA analysis. CTSE score increases in profiles where the level of identity and resilience increases. The impact of profiles and gender interaction on CTSE is significant. Low profile male students have significantly lower CTSE scores than other groups. While profiles affected CT performance significantly, no difference is found by gender.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The dataset analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Ahmed, U., Umrani, W. A., Qureshi, M. A., & Samad, A. (2018). Examining the links between teachers support, academic efficacy, academic resilience, and student engagement in Bahrain. International Journal of Advanced and Applied Sciences, 5(9), 39–46. https://doi.org/10.21833/ijaas.2018.09.008

    Article  Google Scholar 

  • Akaike, H. (1974). A new look at the statistical model identification. Selected Papers of Hirotugu Akaike (pp. 215–222). Springer.

  • Allan, J. F., McKenna, J., & Dominey, S. (2014). Degrees of resilience: profiling psychological resilience and prospective academic achievement in university inductees. British Journal of Guidance & Counselling, 42(1), 9–25. https://doi.org/10.1080/03069885.2013.793784

    Article  Google Scholar 

  • Anderson, R. (2007). Being a mathematics learner: Four faces of identity. The Mathematics Educator, 17, 7–14.

    Google Scholar 

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

    Article  Google Scholar 

  • Ardito, G., Czerkawski, B., & Scollins, L. (2020). Learning computational thinking together: Effects of gender differences in collaborative middle school robotics program. TechTrends, 64(3), 373–387. https://doi.org/10.1007/s11528-019-00461-8

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.

    Google Scholar 

  • Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.

    Google Scholar 

  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Acm Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905

    Article  Google Scholar 

  • Berland, M., & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628–647. https://doi.org/10.1007/s10956-015-9552-x

    Article  Google Scholar 

  • Billett, S. (1996). Situated learning: Bridging sociocultural and cognitive theorising. Learning and Instruction, 6(3), 263–280. https://doi.org/10.1016/0959-4752(96)00006-0

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada (Vol.1, p. 25).

  • Cassidy, S. (2015). Resilience building in students: The role of academic self-efficacy. Frontiers in Psychology, 6, 1781.

    Article  Google Scholar 

  • Cassidy, S. (2016). The Academic Resilience Scale (ARS-30): A new multidimensional construct measure. Frontiers in Psychology, 7, 1787. https://doi.org/10.3389/fpsyg.2016.01787

    Article  Google Scholar 

  • Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001

    Article  Google Scholar 

  • Childs, M. (2011). Identity: A primer. Reinventing ourselves: Contemporary concepts of identity in virtual worlds (pp. 13–31). Springer.

    Chapter  Google Scholar 

  • Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Retrieved from https://www.statmodel.com/download/relatinglca.pdf. Accessed 03.11.2022

  • Çokluk, Ö., Şekercioğlu, G. ve Büyüköztürk, Ş. (2010). Sosyal Bilimler İçin Çok Değişkenli İstatistik: SPSS ve Lisrel Uygulamaları. Pegem.

  • Computer Science Teachers Association. (2011). Operational definition of computational thinking. Retrieved from https://cdn.iste.org/www-root/Computational_Thinking_Operational_Definition_ISTE.pdf. Accessed 03.11.2022

  • Çetin, İ, Otu, T., & Oktaç, A. (2020). Adaption of the computational thinking test into Turkish. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(2), 343–360. https://doi.org/10.16949/turkbilmat.643709

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Etherton, K., Steele-Johnson, D., Salvano, K., & Kovacs, N. (2022). Resilience effects on student performance and well-being: the role of self-efficacy, self-set goals, and anxiety. The Journal of General Psychology, 149(3), 279–298.

    Article  Google Scholar 

  • Handley, K., Sturdy, A., Fincham, R., & Clark, T. (2006). Within and beyond communities of practice: Making sense of learning through participation, identity and practice. Journal of Management Studies, 43(3), 641–653. https://doi.org/10.1111/j.1467-6486.2006.00605.x

    Article  Google Scholar 

  • Israel-Fishelson, R., & Hershkovitz, A. (2022). Studying interrelations of computational thinking and creativity: A scoping review (2011–2020). Computers & Education, 176, 104353. https://doi.org/10.1016/j.compedu.2021.104353

    Article  Google Scholar 

  • Kale, U., Akcaoglu, M., Cullen, T., Goh, D., Devine, L., Calvert, N., & Grise, K. (2018). Computational what? Relating computational thinking to teaching. TechTrends, 62(6), 574–584. https://doi.org/10.1007/s11528-018-0290-9

    Article  Google Scholar 

  • Kalelioğlu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583–596.

    Google Scholar 

  • Keye, M. D., & Pidgeon, A. M. (2013). Investigation of the relationship between resilience, mindfulness, and academic self-efficacy. Open Journal of Social Sciences, 1(6), 1–4.

    Article  Google Scholar 

  • Kim, Y. S., & Merriam, S. B. (2010). Situated learning and identity development in a Korean older adults’ computer classroom. Adult Education Quarterly, 60(5), 438–455. https://doi.org/10.1177/0741713610363019

    Article  Google Scholar 

  • Kong, S. C., & Wang, Y. Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 106230. https://doi.org/10.1016/j.chb.2019.106230

    Article  Google Scholar 

  • Kong, S. C., & Lai, M. (2022). Computational identity and programming empowerment of students in computational thinking development. British Journal of Educational Technology, 53(3), 668–686. https://doi.org/10.1111/bjet.13175

    Article  Google Scholar 

  • Kong, S. C. (2016). A framework of curriculum design for computational thinking development in K-12 education. Journal of Computers in Education, 3(4), 377–394. https://doi.org/10.1007/s40692-016-0076-z

    Article  Google Scholar 

  • Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026

    Article  Google Scholar 

  • Kukul, V., & Karatas, S. (2019). Computational thinking self-efficacy scale: Development, validity and reliability. Informatics in Education, 18(1), 151–164. https://doi.org/10.15388/infedu.2019.07

    Article  Google Scholar 

  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

  • Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1–8. https://doi.org/10.1007/s10956-019-09803-w

    Article  Google Scholar 

  • Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A measurement guide. Journal of Career Assessment, 14(1), 12–35.

    Article  Google Scholar 

  • León Hernández, A., González Escobar, S., Arratia López Fuentes, N. I. G., & Barcelata Eguiarte, B. E. (2019). Stress, self-efficacy, academic achievement and resilience in emerging adults. Electronic Journal of Research in Educational Psychology, 17(1), 129–148.

  • Li, M. H., Eschenauer, R., & Persaud, V. (2018). Between avoidance and problem solving: Resilience, self-efficacy, and social support seeking. Journal of Counseling & Development, 96(2), 132–143.

    Article  Google Scholar 

  • Li, H. (2017). The ‘secrets’ of Chinese students’ academic success: academic resilience among students from highly competitive academic environments. Educational Psychology, 37(8), 1001–1014. https://doi.org/10.1080/01443410.2017.1322179

    Article  Google Scholar 

  • Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). Computational thinking is more about thinking than computing. Journal for STEM Education Research, 3(1), 1–18. https://doi.org/10.1007/s41979-020-00030-2

    Article  Google Scholar 

  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.

    Article  MathSciNet  MATH  Google Scholar 

  • Malekpour Lapari, K., & Reneni, B. (2022). Investigating the mediating role of resilience and academic self-efficacy in the causal model of the effect of teacher-student interaction on students’ academic engagement. Biquarterly Journal of Cognitive Strategies in Learning. https://doi.org/10.22084/J.PSYCHOGY.2022.25646.2427

    Article  Google Scholar 

  • Martin, A. (2002). Motivation and academic resilience: Developing a model for student enhancement. Australian Journal of Education, 46(1), 34–49. https://doi.org/10.1177/000494410204600104

    Article  Google Scholar 

  • Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. D. Little (Ed.), The Oxford handbook of quantitative methods: Statistical analysis (vol 2, pp. 551–611).

  • McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons, Inc.

  • Ononye, U., Ndudi, F., Bereprebofa, D., & Maduemezia, I. (2022). Academic resilience, emotional intelligence, and academic performance among undergraduate students. Knowledge and Performance Management, 6(1), 1. https://doi.org/10.21511/kpm.06(1).2022.01

    Article  Google Scholar 

  • Pajares, F. (2002). Gender and perceived self-efficacy in self-regulated learning. Theory Into Practice, 41(2), 116–125.

    Article  Google Scholar 

  • Pala, F. K., & Mıhcı Türker, P. (2021). The effects of different programming trainings on the computational thinking skills. Interactive Learning Environments, 29(7), 1090–1100. https://doi.org/10.1080/10494820.2019.1635495

    Article  Google Scholar 

  • Park, Y., & Shin, Y. (2019). Comparing the effectiveness of scratch and app inventor with regard to learning computational thinking concepts. Electronics, 8(11), 1269.

    Article  Google Scholar 

  • Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2007). A latent profile analysis of college students’ achievement goal orientation. Contemporary Educational Psychology, 32(1), 8–47.

    Article  Google Scholar 

  • Patton, E. W., Tissenbaum, M., & Harunani, F. (2019). MIT app inventor: Objectives, design, and development. Computational Thinking Education (pp. 31–49). Springer.

    Chapter  Google Scholar 

  • Paton, D., Smith, L., & Violanti, J. (2000). Disaster response: risk, vulnerability and resilience. Disaster Prevention and Management: An International Journal, 9(3), 173–180.

    Article  Google Scholar 

  • Pérez, A. (2018). A framework for computational thinking dispositions in mathematics education. Journal for Research in Mathematics Education, 49(4), 424–461. https://doi.org/10.5951/jresematheduc.49.4.0424

    Article  Google Scholar 

  • Rachmawati, I., Setyosari, P., Handarini, D. M., & Hambali, I. M. (2021). Do social support and self-efficacy correlate with academic resilience among adolescence? International Journal of Learning and Change, 13(1), 49–62. https://doi.org/10.1504/IJLC.2021.111664

    Article  Google Scholar 

  • Rajan, S. K., Harifa, P. R., & Pienyu, R. (2017). Academic resilience, locus of control, academic engagement and self-efficacy among the school children. Indian Journal of Positive Psychology, 8(4), 507–511.

    Google Scholar 

  • Ricketts, S. N., Engelhard Jr, G., & Chang, M. L. (2015). Development and validation of a scale to measure academic resilience in mathematics. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000274

  • Rodríguez-Martínez, J. A., González-Calero, J. A., & Sáez-López, J. M. (2020). Computational thinking and mathematics using Scratch: an experiment with sixth-grade students. Interactive Learning Environments, 28(3), 316–327. https://doi.org/10.1080/10494820.2019.1612448

    Article  Google Scholar 

  • Rojas, L. F. (2015). Factors affecting academic resilience in middle school students: A case study. Gist: Education and Learning Research Journal, 11, 63–78.

    Article  Google Scholar 

  • Román-González, M., Pérez-González, J. C., Moreno-León, J., & Robles, G. (2018). Extending the nomological network of computational thinking with non-cognitive factors. Computers in Human Behavior, 80, 441–459. https://doi.org/10.1016/j.chb.2017.09.030

    Article  Google Scholar 

  • Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047

    Article  Google Scholar 

  • Romano, L., Angelini, G., Consiglio, P., & Fiorilli, C. (2021). Academic resilience and engagement in high school students: The mediating role of perceived teacher emotional support. European Journal of Investigation in Health Psychology and Education, 11(2), 334–344. https://doi.org/10.3390/ejihpe11020025

    Article  Google Scholar 

  • Rudd, G., Meissel, K., & Meyer, F. (2021). Measuring academic resilience in quantitative research: A systematic review of the literature. Educational Research Review, 34, 100402. https://doi.org/10.1016/j.edurev.2021.100402

    Article  Google Scholar 

  • Russell, J. E., Smith, A., & Larsen, R. (2020). Elements of Success: Supporting at-risk student resilience through learning analytics. Computers & Education, 152, 103890. https://doi.org/10.1016/j.compedu.2020.103890

    Article  Google Scholar 

  • Salminen-Karlsson, M. (2006). Situating gender in situated learning. Scandinavian Journal of Management, 22(1), 31–48. https://doi.org/10.1016/j.scaman.2005.05.005

    Article  Google Scholar 

  • Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. https://doi.org/10.1214/aos/117634413651

    Article  MathSciNet  MATH  Google Scholar 

  • Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343. https://doi.org/10.1007/BF0229436052

    Article  Google Scholar 

  • Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition University of Southampton (E-prints) 6pp.

  • Schunk, D. H., & Ertmer, P. A. (2000). Self-regulation and academic learning: Self-efficacy enhancing interventions. Handbook of self-regulation (pp. 631–649). Academic.

  • 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

    Article  Google Scholar 

  • Sun, L., Hu, L., & Zhou, D. (2022). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education, 181, 104457. https://doi.org/10.1016/j.compedu.2022.104457

    Article  Google Scholar 

  • Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798

    Article  Google Scholar 

  • Tankiz, E., & Atman Uslu, N. (2022). Preparing pre-service teachers for computational thinking skills and its teaching: A convergent mixed-method study. Technology Knowledge and Learning, 1–23. https://doi.org/10.1007/s10758-022-09593-y.

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Trigueros, R., Padilla, A., Aguilar-Parra, J. M., Mercader, I., López-Liria, R., & Rocamora, P. (2020). The influence of transformational teacher leadership on academic motivation and resilience, burnout and academic performance. International Journal of Environmental Research and Public Health, 17(20), 7687. https://doi.org/10.3390/ijerph17207687

    Article  Google Scholar 

  • Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345–1360. https://doi.org/10.1177/0735633117746747

    Article  Google Scholar 

  • Tsarava, K., Moeller, K., Román-González, M., Golle, J., Leifheit, L., Butz, M. V., & Ninaus, M. (2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. https://doi.org/10.1016/j.compedu.2021.104425

    Article  Google Scholar 

  • Türkiye Ministry of National Education. (2018). Bilişim Teknolojileri ve Yazilim Dersi Öğretim Programi (Ortaokul 5 ve 6. Sınıflar). Retrieved from http://mufredat.meb.gov.tr/ProgramDetay.aspx?PID=374. Accessed 03.11.2022

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wenger, E. (2000). Communities of practice and social learning system. Organization, 7, 225–246.

    Article  Google Scholar 

  • Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

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

  • Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568. https://doi.org/10.1007/s11528-016-0087-7

    Article  Google Scholar 

  • Yang, C., Zhou, Y., Cao, Q., Xia, M., & An, J. (2019). The relationship between self-control and self-efficacy among patients with substance use disorders: resilience and self-esteem as mediators. Frontiers in Psychiatry, 10, 388.

    Article  Google Scholar 

  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.

    Article  Google Scholar 

  • Zubareva, N., Kapustina, V., & Matyushina, M. (2019). A professional identity and resilience of students. In SHS Web of Conferences (vol 69, p. 00143). EDP Sciences.

Download references

Author information

Authors and Affiliations

  1. Faculty of Education, Computer Education and Instructional Technology, Manisa Celal Bayar University, Manisa, Turkey

    Nilüfer Atman Uslu

Authors
  1. Nilüfer Atman Uslu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nilüfer Atman Uslu.

Ethics declarations

Conflict of interest

There is no conflict of interest to disclose.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Atman Uslu, N. How do computational thinking self-efficacy and performance differ according to secondary school students’ profiles? The role of computational identity, academic resilience, and gender. Educ Inf Technol 28, 6115–6139 (2023). https://doi.org/10.1007/s10639-022-11425-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11425-6

Keywords

Search

Navigation