Promote students’ computational thinking in rural areas: The moderating role of gender

Deddy Barnabas Lasfeto, Eka Budhi Santosa, Tuti Setyorini

Article ID: 8793
Vol 8, Issue 12, 2024

VIEWS - 30 (Abstract) 14 (PDF)

Abstract


The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.


Keywords


computational thinking; gender; rural areas; learning; curriculum

Full Text:

PDF


References


Ahrens, R. de B., Lirani, L. da S., & de Francisco, A. C. (2020). Construct validity and reliability of the work environment assessment instrument WE-10. International Journal of Environmental Research and Public Health, 17(20), 1–19. https://doi.org/10.3390/ijerph17207364

Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Educational Technology and Society, 19(3), 47–57.

Avcı, C., & Deniz, M. N. (2022). Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11078-5

Bhagat, K. K., & Dasgupta, C. (2021). Computational Thinking for Teachers. July, 113.

Bufasi, E., Hoxha, M., Cuka, K., & Vrtagic, S. (2022). Developing Student’s Comprehensive Knowledge of Physics Concepts by Using Computational Thinking Activities: Effects of a 6-Week Intervention. International Journal of Emerging Technologies in Learning, 17(18), 161–176. https://doi.org/10.3991/ijet.v17i18.31743

Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a Generation’s Way of Thinking: Teaching Computational Thinking Through Programming. Review of Educational Research, 87(4), 834–860. https://doi.org/10.3102/0034654317710096

Cui, Z., & Ng, O. L. (2021). The Interplay Between Mathematical and Computational Thinking in Primary School Students’ Mathematical Problem-Solving Within a Programming Environment. Journal of Educational Computing Research, 59(5), 988–1012. https://doi.org/10.1177/0735633120979930

Denning, P. J. (2007). Computing is a natural science. Communications of the ACM, 50(7), 13–18. https://doi.org/10.1145/1272516.1272529

Du, J., & Wimmer, H. (2019). Hour of Code: A Study of Gender Differences in Computing. Information Systems Education Journal, 17(4), 91–100. https://isedj.org/;http://iscap.info

Espino, E. E. E., & González, C. S. G. (2015). Influence of gender on computational thinking. ACM International Conference Proceeding Series, 07-09-Sept(February 2020), 3–5. https://doi.org/10.1145/2829875.2829904

Esteve-Mon, F. M., Llopis, M. A., & Adell-Segura, J. (2020). Digital competence and computational thinking of student teachers. International Journal of Emerging Technologies in Learning, 15(2), 29–41. https://doi.org/10.3991/ijet.v15i02.11588

Ezeamuzie, N. O., & Leung, J. S. C. (2022). Computational Thinking Through an Empirical Lens: A Systematic Review of Literature. Journal of Educational Computing Research, 60(2), 481–511. https://doi.org/10.1177/07356331211033158

Gao, X., & Hew, K. F. (2022). Toward a 5E-Based Flipped Classroom Model for Teaching Computational Thinking in Elementary School: Effects on Student Computational Thinking and Problem-Solving Performance. Journal of Educational Computing Research, 60(2), 512–543. https://doi.org/10.1177/07356331211037757

Günbatar, M. S. (2019). Computational thinking within the context of professional life: Change in CT skill from the viewpoint of teachers. Education and Information Technologies, 24(5), 2629–2652. https://doi.org/10.1007/s10639-019-09919-x

Guzdial, M. (2008). Education: Paving the way for computational thinking. Communications of the ACM, 51(8), 25–27. https://doi.org/10.1145/1378704.1378713

Ikolo, V. E., & Okiy, R. B. (2012). Gender differences in computer literacy among clinical medical students in selected Southern Nigerian Universities. Library Philosophy and Practice, 2012(MAY).

Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021). A Log-Based Analysis of the Associations Between Creativity and Computational Thinking. Journal of Educational Computing Research, 59(5), 926–959. https://doi.org/10.1177/0735633120973429

Jamal, N. N., Jawawi, D. N. A., Hassan, R., & Mamat, R. (2021). Conceptual Model of Learning Computational Thinking Through Educational Robotic. International Journal of Emerging Technologies in Learning, 16(15), 91–106. https://doi.org/10.3991/ijet.v16i15.24257

Kalelioʇlu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047

Kanaki, K., & Kalogiannakis, M. (2022). Assessing Algorithmic Thinking Skills in Relation to Gender in Early Childhood. Educational Process: International Journal, 11(2), 44–59. https://doi.org/10.22521/edupij.2022.112.3

Kang, C., Liu, N., Zhu, Y., Li, F., & Zeng, P. (2023). Developing College students’ computational thinking multidimensional test based on Life Story situations. Education and Information Technologies, 28(3), 2661–2679. https://doi.org/10.1007/s10639-022-11189-z

Lai, R. P. Y. (2019). What underlies computational thinking: Exploring its cognitive mechanism and educational implications. Proceedings of International Conference on Computational Thinking Education, 204–208.

Lasfeto, D. B., Setyosari, P., Djatmika, E. T., & Ulfa, S. (2018). Learning preference assessment: A fuzzy logic approach. Journal of Theoretical and Applied Information Technology, 96(10), 2862–2871.

Mbukanma, I., & Strydom, K. (2022). Challenges to and Enablers of Women’s Advancement in Academic Careers at a Selected South African University. International Journal of Learning, Teaching and Educational Research, 21(12), 44–64. https://doi.org/10.26803/ijlter.21.12.3

Mouza, C., Yang, H., Pan, Y. C., Yilmaz Ozden, S., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 61–76. https://doi.org/10.14742/ajet.3521

Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry, 11(1), 1–17. https://doi.org/10.1080/20004508.2019.1627844

Sandygulova, A., & O’Hare, G. M. P. (2018). Age- and Gender-Based Differences in Children’s Interactions with a Gender-Matching Robot. International Journal of Social Robotics, 10(5), 687–700. https://doi.org/10.1007/s12369-018-0472-9

Selby, C. (2013). Computational Thinking : The Developing Definition. ITiCSE Conference 2013, 5–8.

Showkat, D., & Grimm, C. (2018). Identifying Gender Differences in Information Processing Style, Self-efficacy, and Tinkering for Robot Tele-operation. 2018 15th International Conference on Ubiquitous Robots, UR 2018, 443–448. https://doi.org/10.1109/URAI.2018.8441766

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

Stoilescu, D., & Egodawatte, G. (2010). Gender differences in the use of computers, programming, and peer interactions in computer science classrooms. Computer Science Education, 20(4), 283–300. https://doi.org/10.1080/08993408.2010.527691

Sun, L., Hu, L., & Zhou, D. (2022). Single or Combined? A Study on Programming to Promote Junior High School Students’ Computational Thinking Skills. Journal of Educational Computing Research, 60(2), 283–321. https://doi.org/10.1177/07356331211035182

Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers and Education, 148(May 2019), 103798. https://doi.org/10.1016/j.compedu.2019.103798

Tedre, M., & Denning, P. J. (2016). The long quest for computational thinking. ACM International Conference Proceeding Series, 120–129. https://doi.org/10.1145/2999541.2999542

Théry-Schultz, J. (2018). Les enjeux de la clarification des règles. Concurrences, 2018(3), 22–24.

Tsai, M. J., Liang, J. C., & Hsu, C. Y. (2021). The Computational Thinking Scale for Computer Literacy Education. Journal of Educational Computing Research, 59(4), 579–602. https://doi.org/10.1177/0735633120972356

Tsai, M. J., Liang, J. C., Lee, S. W. Y., & Hsu, C. Y. (2022). Structural Validation for the Developmental Model of Computational Thinking. Journal of Educational Computing Research, 60(1), 56–73. https://doi.org/10.1177/07356331211017794

Wu, S. Y., & Su, Y. S. (2021). Visual Programming Environments and Computational Thinking Performance of Fifth- and Sixth-Grade Students. Journal of Educational Computing Research, 59(6), 1075–1092. https://doi.org/10.1177/0735633120988807

Yang, J., Yu, H., & Chen, N. shing. (2019). Using blended synchronous classroom approach to promote learning performance in rural area. Computers and Education, 141(July), 103619. https://doi.org/10.1016/j.compedu.2019.103619

Zha, S., Morrow, D. A. L., Curtis, J., & Mitchell, S. (2021). Learning Culture and Computational Thinking in a Spanish Course: A Development Model. Journal of Educational Computing Research, 59(5), 844–869. https://doi.org/10.1177/0735633120978530




DOI: https://doi.org/10.24294/jipd.v8i12.8793

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Deddy Barnabas Lasfeto, Eka Budhi Santosa, Tuti Setyorini

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.