Harnessing Digital Skills For Academic Success: Unveiling The Power of Learning Motivation in Computational Thinking and Tech Integration
DOI:
https://doi.org/10.58524/oler.v4i2.501Keywords:
Computational Thinking, Learning Motivation, Student Performance, Technology Integration.Abstract
The workforce's demand for critical thinking and innovation highlights the need to improve students' problem-solving skills, thus encouraging educational institutions to adopt technology-based strategies for an engaging learning environment. Previous studies have explored the relationship between learning motivation and academic outcomes and the role of technology and web-based media in improving problem-solving skills. However, limited research has comprehensively examined the interaction between computational thinking, technology integration, learning motivation, and student performance. This study aims to examine how Computational Thinking (CT) and Technology Integration (TI) influence Learning Motivation (LM) and Student Performance (SP), providing insights into optimizing digital skills for academic success in the digital age. Data were collected from 426 respondents' university students in Indonesia randomly. A questionnaire with a 5-point Likert scale consisting of several variables such as Computational Thinking, Technology Integration, Learning Motivation, and Student Performance were used in this study. Then, the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to check the measurement and assessment model. The results showed that CT and TI positively and significantly impacted LM and SP. In addition, LM serves as an important mediator, strengthening the influence of CT and IT on academic outcomes. Specifically, technology integration has a greater impact on LM than CT, while LM significantly improves SP. This study presents a detailed framework for educators to enhance learning experiences by integrating digital skills and fostering student motivation. The findings offer practical implications for developing effective educational strategies that meet the changing demands of the digital age. Future research is recommended to investigate the long-term effects of CT and IT in various educational environments.
References
Agortey, J. J., Maundu, J. N., Nyamu, F. K., & Muindi, D. M. (2023). Demographic difference in stress coping and student-athletes in colleges of education in ghana. European Journal of Contemporary Education and E-Learning, 1(2), 45–53. https://doi.org/10.59324/ejceel.2023.1(2).05
Albra, W., Muchtar, D., Nurlela, N., Muliani, M., Safitri, R., & Nisa, F. Z. (2023). The role of halal awareness on the relationship between religiosity and halal purchase intention. International Journal of Islamic Economics and Finance (IJIEF), 6(2), 312–336. https://doi.org/10.18196/ijief.v6i2.16685
Alegre, E. M. (2023). Technology-driven education: Analyzing the synergy among innovation, motivation, and student engagement. International Journal of Membrane Science and Technology, 10(2), 1477–1485. https://doi.org/10.15379/ijmst.v10i2.1507
Alfares, N. (2021). The effect of problem-based learning on students problem-solving self-efficacy through blackboard system in higher education. International Journal of Education and Practice, 9(1), 185–200. https://doi.org/10.18488/journal.61.2021.91.185.200
Alghamdi, M. I. (2020). Assessing factors affecting intention to adopt ai and ml: the case of the jordanian retail industry. MENDEL, 26(2), 39–44. https://doi.org/10.13164/mendel.2020.2.039
Alianti, S., Helwen Heri, Hadiyati, & Agus Seswandi. (2023). The Impact of Work Culture and Attitudes on Job Satisfaction and Their Impact on The Performance of Mandau Regional General Hospital Employee. Jurnal Manajemen Dan Bisnis Terapan, 5(1), 50–62. https://doi.org/10.31849/jmbt.v5i1.14550
Alshammari, A. M., Alshammari, F. F., Thomran, M., & Altwaiji, M. (2023). Integrating technological knowledge into higher education curricula: an essential measure for attaining sustainable development in saudi arabia. Sustainability, 15(22), 15956. https://doi.org/10.3390/su152215956
Atma, B. A., Azahra, F. F., & Mustadi, A. (2021). Teaching style, learning motivation, and learning achievement: Do they have significant and positive relationships? Jurnal Prima Edukasia, 9(1) 23-31. https://doi.org/10.21831/jpe.v9i1.33770
Aulia Khoirunnisa & Usman. (2024). Effect positive of learning agility on salespeople turnover intention in the automotive sector. Neo Journal of Economy and Social Humanities, 3(1), 1–11. https://doi.org/10.56403/nejesh.v3i1.159
Bachmid, S., & Noval, N. (2023). Moderate role of halal awareness in the relationship of purchase intention, personal norms and muslim buying behavior. BISNIS : Jurnal Bisnis Dan Manajemen Islam, 10(2), 247. https://doi.org/10.21043/bisnis.v10i2.16653
Başaran, M., & İlter, M. (2023). Investigation of the relationship between teachers’ inquiry-based teaching self-efficiency for STEM+S and their computational thinking skills. Research on Education and Media, 15(2), 1–9. https://doi.org/10.2478/rem-2023-0019
Boateng, S., & Kalonde, G. (2024). Exploring the synergy of the SAMR (Substitution, augmentation, modification, and redefinition) model and technology integration in education: the key to unlocking student engagement and motivation. Proceedings of The International Conference on Advanced Research in Education, Teaching, and Learning, 1(1), 37–46. https://doi.org/10.33422/aretl.v1i1.185
Cabezas-González, M., Casillas-Martín, S., & García-Valcárcel Muñoz-Repiso, A. (2021). Basic Education Students’ Digital Competence in the Area of Communication: The Influence of Online Communication and the Use of Social Networks. Sustainability, 13(8), 4442. https://doi.org/10.3390/su13084442
Ching, Y., Hsu, Y.-C., & Baldwin, S. (2018). Developing Computational Thinking With Educational Technologies for Young Learners. Techtrends, 62(6), 563–573. https://doi.org/10.1007/s11528-018-0292-7
Creswell, J. W. (2009). Research Design Qualitative, Quantitative, and Mixed Methods Approaches (3rd ed.). SAGE.
Denning, P. J., & Tedre, M. (2019). Computational Thinking. https://doi.org/10.7551/mitpress/11740.001.0001
Díaz-Fúnez, P. A., Cardella, G. M., Hernández-Sánchez, B. R., Sánchez-García, J. C., & Mañas-Rodríguez, M. Á. (2024). Is adding resources always beneficial? Multiplicative impact of psychological capital and goal-oriented climate on Spanish public worker satisfaction and engagement. Frontiers in Psychology, 15(1), 1418409. https://doi.org/10.3389/fpsyg.2024.1418409
Emda, A. (2018). Kedudukan motivasi belajar siswa dalam pembelajaran. Lantanida Journal, 5(2), 172. https://doi.org/10.22373/lj.v5i2.2838
Fadila, F., Soleha, S., Febriansyah, F., & Azwar, B. (2022). Counseling guidance services in improving learning motivation post covid 19. AL-ISHLAH: Jurnal Pendidikan, 14(4), 6411–6418. https://doi.org/10.35445/alishlah.v14i4.2683
Fauzi, A., Zahroh, S. H., & Ekawati, E. Y. (2022). The influence of using module with computational thinking unplugged approaches and module with scientific approaches based on student’s critical thinking ability towards cognitive ability the subject of temperature and heat transfer. Widyagogik : Jurnal Pendidikan Dan Pembelajaran Sekolah Dasar, 10(1), 234–248. https://doi.org/10.21107/widyagogik.v10i1.17587
Filgona, J., Sakiyo, J., Gwany, D. M., & Okoronka, A. U. (2020). Motivation in learning. Asian Journal of Education and Social Studies, 10 (4). 16–37. https://doi.org/10.9734/ajess/2020/v10i430273
Frameiliada, D., Setiawan, S., Azizah, T., & Margarida, K. (2023). Learning facilities in supporting the process learning and learning motivation. Scientechno: Journal of Science and Technology, 2(2), 118–124. https://doi.org/10.55849/scientechno.v2i2.162
Furtasan Ali Yusuf, Linda Wijayanti, L Lukas, Sandra Octaviani, & Enny Widawati. (2023). Applications of educational technology in solving learning problems. Athena: Journal of Social, Culture and Society, 1(4), 253–256. https://doi.org/10.58905/athena.v1i4.201
Gong, D., Yang, H. H., & Cai, J. (2020). Exploring the key influencing factors on college students’ computational thinking skills through flipped-classroom instruction. International Journal of Educational Technology in Higher Education, 17(1), 19. https://doi.org/10.1186/s41239-020-00196-0
Hsieh, M.-C., Pan, H.-C., Hsieh, S.-W., Hsu, M.-J., & Chou, S.-W. (2022). Teaching the concept of computational thinking: a stem-based program with tangible robots on project-based learning courses. Frontiers in Psychology, 12(1), 828568. https://doi.org/10.3389/fpsyg.2021.828568
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
Kumar, P., Dwivedi, Y. K., & Anand, A. (2023). Responsible artificial intelligence (ai) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement. Information Systems Frontiers, 25(6), 2197–2220. https://doi.org/10.1007/s10796-021-10136-6
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727X.2015.1005806
Lian, Q., Xia, L., & Wu, D. (2022). Assessing anxiety during the COVID-19 delta epidemic: Validation of the Chinese coronavirus anxiety scale. Frontiers in Psychology, 13(1), 981121. https://doi.org/10.3389/fpsyg.2022.981121
Liu, S., Peng, C., & Srivastava, G. (2023). What influences computational thinking? A theoretical and empirical study based on the influence of learning engagement on computational thinking in higher education. Computer Applications in Engineering Education, 31(6), 1690–1704. https://doi.org/10.1002/cae.22669
M. Albay, E. (2020). Investigating the effects of the problem-solving approach among tertiary education students. Proceedings of The 2nd World Conference on Research in Teaching and Education. 9(1) pp 76-87. https://doi.org/10.33422/2nd.worldte.2020.09.246
Maharani, S., Kholid, M. N., Pradana, L. N., & Nusantara, T. (2019). PROBLEM SOLVING IN THE CONTEXT OF COMPUTATIONAL THINKING. Infinity Journal, 8(2), 109. https://doi.org/10.22460/infinity.v8i2.p109-116
Maryansyah, Y., & Danim, S. (2024). Experiences, perceptions, and challenges of indonesian efl university students with online assessment in the digital age. In M. Kristiawan, N. D. Lestari, D. Samitra, Z. F. Rozi, M. N. Naser, R. M. Valianti, M. Muthmainnah, B. Badeni, F. A. Yanti, D. Apryani, O. L. Agusta, J. Siska, E. Viona, E. Purwandari, & R. D. Riastuti (Eds.), Online Conference of Education Research International Vol. 775(1), pp. 719–732 Atlantis Press SARL. https://doi.org/10.2991/978-2-38476-108-1_71
Mohd Dzin, N. H., & Lay, Y. F. (2021). Validity and reliability of adapted self-efficacy scales in malaysian context using PLS-SEM approach. Education Sciences, 11(11), 676. https://doi.org/10.3390/educsci11110676
Mukasheva, M., & Omirzakova, A. (2021). Computational thinking assessment at primary school in the context of learning programming. World Journal on Educational Technology: Current Issues, 13(3), 336–353. https://doi.org/10.18844/wjet.v13i3.5918
Muttaqin, I., Tursina, N., Sudrajat, A., Yuliza, U., Novianto, N., Ramadhan, F. F., & Kurnanto, M. E. (2023). The effect of academic supervision, managerial competence, and teacher empowerment on teacher performance: The mediating role of teacher commitment. 12(1), 743 https://doi.org/10.12688/f1000research.128502.2
Nong, X. (2023). A Study of the motivation of the chinese college english learners. In B. Majoul, D. Pandya, & L. Wang (Eds.), Proceedings of the 2022 4th International Conference on Literature, Art and Human Development. Atlantis Press SARL 4(1), 596-605. https://doi.org/10.2991/978-2-494069-97-8_76
Nurdin, A. A., & Abidin, Z. (2023). The influence of recommendation system quality on e-commerce customer loyalty with cognition affective behavior theory. Journal of Advances in Information Systems and Technology, 5(1), 1–11. https://doi.org/10.15294/jaist.v5i1.65910
Ozili, P. K. (2022). The acceptable R-square in empirical modelling for social science research. SSRN Electronic Journal, January 2022. 1(1) https://doi.org/10.2139/ssrn.4128165
Park, W., & Kwon, H. (2023). Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education. 34, 109–135 https://doi.org/10.1007/s10798-023-09812-2
Pathan, H., Moskvitcheva, S. A., Khatoon, S., & Aleksandrova, O. I. (2024). The relationship between teachers’ motivation, professional development, and mobile technology integration in language learning. International Journal of Interactive Mobile Technologies (iJIM), 18(09), 50–60. https://doi.org/10.3991/ijim.v18i09.48865
Petrusly, P., Lambertus Kollo, F., D. S. Bani, M., Mahfud, T., & Zulkarnain, Z. (2024). The effect of gamification using kahoot on students’ critical thinking abilities: The role of mediating learning engagement and motivation. The Effect of Gamification Using Kahoot on Students’ Critical Thinking Abilities: The Role of Mediating Learning Engagement and Motivation. 30(5) 953–963. https://doi.org/10.53555/kuey.v30i5.1524
Piliang, I. W., Rusdi, R., & Miarsyah, M. (2019). Correlation between learning motivation and learning outcomes in circulation system learning materials in grade xi. Indonesian Journal of Science and Education, 3(1), 15. https://doi.org/10.31002/ijose.v3i1.861
Prihandoko, L. A., Morganna, R., & Nugrah Amalia, S. (2024). Self-efficacy and metacognition as the mediated effects of growth mindset on academic writing performance. Journal of Language and Education, 10(2), 108–122. https://doi.org/10.17323/jle.2024.13979
Puput Iswandyah Raysharie, Luluk Tri Harinie, Nathaly Inglesia, Vita, V., Santi Wati, Benedikta Sianipar, Ongki, O., Rihan Pasha, Muhammad Abdurrahman, Kemas Ary Fadilla, & Febriana Putri. (2023). The effect of student’s motivation on academic achievement. Journal Pendidikan Ilmu Pengetahuan Sosial, 15(1), 168–175. https://doi.org/10.37304/jpips.v15i1.9552
Rabaa’i, A. A., AlMaati, S. A., & Zhu, X. (2021). Students’ continuance intention to use moodle: An expectation-confirmation model approach. Interdisciplinary Journal of Information, Knowledge, and Management, 16(August), 397–434. https://doi.org/10.28945/4842
Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & Van Smeden, M. (2020). Calculating the sample size required for developing a clinical prediction model. BMJ, m441. 12
(1) 368 https://doi.org/10.1136/bmj.m441
Şahi̇N, H. (2021). The effect os stem-based education program on problem solving skills of five year old children. Malaysian Online Journal of Educational Technology, 9(4), 68–87. https://doi.org/10.52380/mojet.2021.9.4.325
Saregar, A., Kirana, L. J., Asyhari, A., Anugrah, A., Fitri, M. R., & Panse, V. R. (2024). Technology and Digital Literacy: Interrelationships and the Impact of Acceptance with Self-regulated Learning. E3S Web of Conferences, 10, 482, 04006. https://doi.org/10.1051/e3sconf/202448204006
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. Handbook of Market Research, November,319(3) 587–632. https://doi.org/10.1007/978-3-319-57413-4_15
Seechaliao, T. (2017). Instructional strategies to support creativity and innovation in education. Journal of Education and Learning, 6(4), 201. https://doi.org/10.5539/jel.v6n4p201
Setiawan, H., Handican, R., & Rurisman, R. (2023). Revolutionizing math education: unleashing the potential of web-based learning media for enhanced mathematical problem solving skills. JDIME : Journal of Development and Innovation in Mathematics Education, 1(2), 01–11. https://doi.org/10.32939/jdime.v1i2.2978
Snell, K. I. E., Archer, L., Ensor, J., Bonnett, L. J., Debray, T. P. A., Phillips, B., Collins, G. S., & Riley, R. D. (2021). External validation of clinical prediction models: Simulation-based sample size calculations were more reliable than rules-of-thumb. Journal of Clinical Epidemiology, 135(1), 79–89. https://doi.org/10.1016/j.jclinepi.2021.02.011
Sun, J. (2021). Lecture-based, problem-based, digital problem-based and distance learning on knowledge improvement in medical education: A meta-analysis. 5 (1), 21 https://doi.org/10.1101/2021.05.26.445870
Syawallina, N., & Suganda, S. P. (2023). TPACK-EFL for the improvement of the english teacher education program. In S. M. G. Tambunan (Ed.), Proceedings of the fourth Asia-Pacific Research in Social Sciences and Humanities, Arts and Humanities Stream (AHS-APRISH 2019) (Vol. 753, pp. 281–292). Atlantis Press SARL. https://doi.org/10.2991/978-2-38476-058-9_22
Taupik, R. P., & Fitria, Y. (2023). Learning motivation and computational thinking ability of elementary school students in learning science. Jurnal Penelitian Pendidikan IPA, 9(9), 7665–7671. https://doi.org/10.29303/jppipa.v9i9.4826
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in k–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9(1), 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962
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
Turgut, O., & Ocak, G. (2017). The examination of the relation between teacher candidates’ problem solving appraisal and utilization of motivated strategies for learning. Journal of Education and Training Studies, 5(10), 76. https://doi.org/10.11114/jets.v5i10.2550
Varma, A. (2019). Do culturally intelligent management accountants share more knowledge?—the mediating role of coopetition as evident from pls sem and fsQCA. Theoretical Economics Letters, 09(01), 100–118. https://doi.org/10.4236/tel.2019.91009
Wang, Y. (2023). Comparing the differences in the main factors influencing high school and college students’ involvement in cyberbullying behaviour—based on bandura’s social cognitive theory. Lecture Notes in Education Psychology and Public Media, 5(1), 524–533. https://doi.org/10.54254/2753-7048/5/20220691
Yanti, G. S., & Nurhidayah, R. (2020). Practices on technology integration in elt: a review on existing researches. Briliant: Jurnal Riset Dan Konseptual, 5(2), 292. https://doi.org/10.28926/briliant.v5i2.467
Zhou, S., & Wang, Y. (2022). How negative anthropomorphic message framing and nostalgia enhance pro-environmental behaviors during the COVID-19 pandemic in China: An SEM-NCA approach. Frontiers in Psychology, 13, 977381. 1664-1078 https://doi.org/10.3389/fpsyg.2022.977381
Downloads
Published
Issue
Section
License
Online Learning in Educational Research is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Online Learning in Educational Research agree to the following terms:
Copyright Retention: Authors retain the copyright of their work without any restrictions.
Publishing Rights: Authors retain the right to publish and distribute their work without any restrictions.
License Agreement: By publishing with Online Learning in Educational Research, authors agree that their work will be licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license allows others to share and adapt the work, provided that appropriate credit is given, any changes are indicated, and the new creations are licensed under the same terms.
