Empowering Problem-solving in Computer Science: A Need Analysis for a Computational Thinking Mobile Learning Application

  • Mimi Zairul Mohmad Fuzi
  • Wan Ahmad Jaafar Wan Yahaya

Abstract

The advancement of digital technology has revolutionized education across all levels, giving rise to digital learning as a novel pedagogical environment. Among the subjects at the forefront of this transformation is computer science, introduced in matriculation colleges to supplant traditional information technology courses. However, computer science poses inherent complexity, demanding abstract thinking and diverse problem-solving methodologies. Computational thinking (CT) emerges as a promising approach to address these challenges, recognized as a vital skillset for fostering innovation in digital technology among students. The ubiquity of smartphones and mobile internet facilitates the adoption of mobile learning, offering students the flexibility to access educational content anytime, anywhere. Consequently, this need analysis study aims to assess current teaching practices in computer science and identify the need for mobile learning applications that integrate CT as a problem-solving technique among matriculation college students. Interviews with three computer science lecturers revealed a reliance on conventional pedagogy with limited blended learning approaches through college portals. Notably, specific techniques for imparting programming problem-solving skills were lacking. Nonetheless, it is noteworthy that all instructors recognized the considerable potential of mobile learning applications in effectively engaging students and facilitating the development of problem-solving proficiency.

References

Alessi, S. M., & Trollip, S. R. (2011). Multimedia for learning: Methods and development (3rd ed.). (3rd ed.). MA: Allyn & Bacon.
Baker, E. ., & Mayer, R. . (1999). Computer-based assessment of problem solving. Computers in Human Behavior, 15(3–4), 269–282. https://doi.org/10.1016/S0747-5632(99)00023-0
Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 12(2), 272. https://doi.org/10.30935/cedtech/8247
En, L. K., Karpudewan, M., & Zaharudin, R. (2021). Computational thinking in STEM education among matriculation science students. Asia Pacific Journal of Educators and Education, 36(1), 177–194. https://doi.org/10.21315/apjee2021.36.1.10
Han, S., & Yi, Y. J. (2019). How does the smartphone usage of college students affect academic performance? Journal of Computer Assisted Learning, 35(1), 13–22. https://doi.org/10.1111/jcal.12306
John Lemay, D., Basnet, R. B., Doleck, T., Bazelais, P., & Saxena, A. (2021). Instructional interventions for computational thinking: Examining the link between computational thinking and academic performance. Computers and Education Open, 2, 100056. https://doi.org/10.1016/j.caeo.2021.100056
Kirwan, C., Costello, E., & Donlon, E. (2022). ADAPTTER: Developing a Framework for Teaching Computational Thinking in Second-Level Schools by Design Research. TechTrends, 66(3), 495–509. https://doi.org/10.1007/s11528-022-00735-8
Lu, Y., & Xiong, T. (2023). The attitudes of high school students and teachers toward mobile apps for learning English: A Q methodology study. Social Sciences and Humanities Open, 8(1), 100555. https://doi.org/10.1016/j.ssaho.2023.100555
Mhd Alkasirah, N. A., Sanmugam, M., & ... (2022). The use of digital video in mobile application for the Arabic Language: A preliminary study. … Journal of Research …, 06(02), 149–162. http://ppsfip.ppj.unp.ac.id/index.php/ijrice/article/view/500%0Ahttp://ppsfip.ppj.unp.ac.id/index.php/ijrice/article/download/500/170
Mohaghegh, M., & Mccauley, M. (2016). Computational Thinking: The Skill Set of the 21st Century. International Journal of Computer Science and Information Technologies, 7(3), 1524–1530. www.ijcsit.com
Mohtar, S., Jomhari, N., Mustafa, M. B., & Yusoff, Z. M. (2023). Mobile learning: research context, methodologies and future works towards middle-aged adults – a systematic literature review. Multimedia Tools and Applications, 82(7), 11117–11143. https://doi.org/10.1007/s11042-022-13698-y
Mourtos, N., Okamoto, N., & Rhee, J. (2004). Defining, teaching, and assessing problem solving skills. 7th UICEE Annual Conference on …, February 2004, 9–13. http://ae.sjsu.edu/files/public/nikos/backup/pdf/UICEE 04 Mumbai.pdf
Poláková, P. (2022). Use of a mobile learning application in the process of foreign vocabulary learning. Procedia Computer Science, 207(Kes), 64–70. https://doi.org/10.1016/j.procs.2022.09.038
Seibert, S. A. (2021). Problem-based learning: A strategy to foster generation Z’s critical thinking and perseverance. Teaching and Learning in Nursing, 16(1), 85–88. https://doi.org/10.1016/j.teln.2020.09.002
Shaharim, S. A., Ishak, N. A., Zaharudin, R., & Wab Mohamed Salleh, W. N. (2022). the Development of Integrated Mobile Game-Based Learning in Psycho-B’Greatmodule: a Needs Analysis. Global Journal of …, 2(3), 312–328. https://www.myedujournal.com/index.php/edugermane/article/view/155%0Ahttps://www.myedujournal.com/index.php/edugermane/article/download/155/141
Snalune, P. (2015). The Benefits of Computational Thinking. ITNOW, 57(4), 58–59. https://doi.org/10.1093/itnow/bwv111
Suartama, I. K., Setyosari, P., Sulthoni, & Ulfa, S. (2019). Development of an instructional design model for mobile blended learning in higher education. International Journal of Emerging Technologies in Learning, 14(16), 4–22. https://doi.org/10.3991/ijet.v14i16.10633
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Vaicondam, Y., Sikandar, H., Irum, S., Khan, N., & Qureshi, M. I. (2022). Research Landscape of Digital Learning Over the Past 20 Years: A Bibliometric and Visualisation Analysis. International Journal of Online and Biomedical Engineering, 18(8), 4–22. https://doi.org/10.3991/ijoe.v18i08.31963
Vogelzang, J., Admiraal, W. F., & van Driel, J. H. (2019). Scrum methodology as an effective scaffold to promote students’ learning and motivation in context-based secondary chemistry education. Eurasia Journal of Mathematics, Science and Technology Education, 15(12). https://doi.org/10.29333/ejmste/109941
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35.
Zahara, M. N., Hendrayana, A., & Pamungkas, A. S. (2020). The Effect of Problem-based Learning Model Modified by Cognitive Load Theory on Mathematical Problem Solving Skills. Hipotenusa : Journal of Mathematical Society, 2(2), 41–55. https://doi.org/10.18326/hipotenusa.v2i2.41-55
Published
2024-03-31
How to Cite
MOHMAD FUZI, Mimi Zairul; WAN YAHAYA, Wan Ahmad Jaafar. Empowering Problem-solving in Computer Science: A Need Analysis for a Computational Thinking Mobile Learning Application. Asian Journal of Research in Education and Social Sciences, [S.l.], v. 6, n. 1, p. 408-417, mar. 2024. Available at: <https://myjms.mohe.gov.my/index.php/ajress/article/view/26217>. Date accessed: 19 may 2024.
Section
English Section