The effects of machine-learning programming simulator on university students’ engagement in learning programming

Kesan simulator pengaturcaraan pembelajaran mesin terhadap penglibatan pelajar universiti

Authors

  • Tansa Trisna Astono Putri Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, 11800 Penang, Malaysia. https://orcid.org/0000-0001-5319-4804
  • Wan Ahmad Jaafar Wan Yahaya Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, 11800 Penang, Malaysia. https://orcid.org/0000-0002-8605-0062
  • Nur Azlina Mohamed Mokmin Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, 11800 Penang, Malaysia. https://orcid.org/0000-0003-1411-5557
  • Sriadhi Sriadhi Information Technology and Computer Education Study Program, Universitas Negeri Medan, Medan, Indonesia.
  • Muhammad Fadhiel Alie Information System Study Program, Universitas Indo Global Mandiri, Palembang, Indonesia.

DOI:

https://doi.org/10.53840/attarbawiy.v8i2.228

Keywords:

engagement, university students, Machine-learning simulator, programming

Abstract

A computer programming course is essential for students pursuing a computer science major. The college offers a course that imparts essential knowledge to undergraduate computer science students. After finishing their degree, individuals may utilize their experience in entrepreneurial pursuits. Students without competency in the fundamental concepts of computer programming may face challenges in obtaining employment, particularly in positions such as developers or software developers. Enrollment in the programming course is compulsory for computer science students. A decline in employment for computer programmers is a recent issue nowadays, while growing demand for skilled programmers happened in certain regions, such as Indonesia. While it is true that many simpler duties can be automated, there will be a growing demand for positions that require strategic decision-making. To remain competitive, programmers must increase their abilities to satisfy these obligations. The study emphasizes the need for integrating technology, including gamification and simulation tools, in programming education to enhance student engagement and learning outcomes. A quasi-experimental study was conducted to investigate the impact of a machine learning (ML) programming simulator on student engagement. The study involved 120 university students, divided into two groups: one using the ML programming simulator and the other using a non-ML simulator. The results showed that students using the ML simulator had higher engagement levels compared to those using the non-ML simulator, suggesting that ML-based tools can significantly improve student participation and learning in programming courses. The findings underscore the potential benefits of using advanced technological tools to foster better learning experiences in computer programming education.

Downloads

Download data is not yet available.

References

Alturki, R. A. (2016). Measuring and improving student performance in an introductory programming course. Informatics in Education, 15(2), 183–204. https://doi.org/10.15388/infedu.2016.10

Altun, H., & Serin, O. (2019). Determination of learning styles and achievements of talented students in the fields of Science and Mathematics. In Cypriot Journal of Educational Sciences (Vol. 14, Issue 1). www.cjes.eu

Angeli, C., & Valanides, N. (2020). Computers in Human Behavior Developing young children ’ s computational thinking with educational robotics : An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105(March 2019), 105954. https://doi.org/10.1016/j.chb.2019.03.018

Ashmore, R., Calinescu, R., & Paterson, C. (2021). Assuring the Machine Learning Lifecycle : Desiderata , Methods , and Challenges. ACM Computing Surveys, 54(5).

Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224

Axelson, R. D., & Flick, A. (2010). Defining Student Engagement. Change: The Magazine of Higher Learning, 43(1), 38–43. https://doi.org/10.1080/00091383.2011.533096

Azzi, I., Jeghal, A., Radouane, A., Yahyaouy, A., & Tairi, H. (2020). A robust classification to predict learning styles in adaptive E-learning systems. Education and Information Technologies, 25(1), 437–448. https://doi.org/10.1007/s10639-019-09956-6

Baist, A., & Pamungkas, A. S. (2017). Analysis of Student Difficulties in Computer Programming. VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro, 2(2), 81. https://doi.org/10.30870/volt.v2i2.2211

Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. ACM SIGCSE Bulletin, 39(2), 32–36. https://doi.org/10.1145/1272848.1272879

Budi, A. H. S., Juanda, E. A., Fauzi, D. L. N., Henny, H., & Masek, A. (2021). Implementation of simulation software on vocational high school students in programming and arduino microcontroller subject. Journal of Technical Education and Training, 13(3), 108–114. https://doi.org/10.30880/jtet.2021.13.03.010

Bond, M. (2020). Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers and Education, 151. https://doi.org/10.1016/j.compedu.2020.103819

Deborah, J. L., Baskaran, R., & Kannan, A. (2014). Learning styles assessment and theoretical origin in an E-learning scenario: a survey. Artificial Intelligence Review, 42(4), 801–819. https://doi.org/10.1007/s10462-012-9344-0

Dixson, M. D. (2015). Measuring student engagement in the online course: the Online Student Engagement scale (OSE). (Section II: Faculty Attitudes and Student Engagement)(Report). Online Learning Journal (OLJ), 19(4), 143.

Edwards, J., Hart, K., & Warren, C. (2020). A Practical Model of Student Engagement While Programming. SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 413–419. https://doi.org/10.1145/3328778.3366863

Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational Research (10th ed.). Pearson.

Harimurti, R., Ekohariadi, Munoto, & Asto Buditjahjanto, I. G. P. (2021). Integrating k-means clustering into automatic programming assessment tool for student performance analysis. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1389–1395. https://doi.org/10.11591/ijeecs.v22.i3.pp1389-1395

Isiaq, O., & Jamil, M. G. (2017). Exploring student engagement in programming sessions using a simulator. Icicte 2017, 206–215.

Jamil, M. G., & Isiaq, S. O. (2019). Teaching technology with technology: approaches to bridging learning and teaching gaps in simulation-based programming education. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0159-9

Khlaif, Z. N., Salha, S., & Kouraichi, B. (2021). Emergency remote learning during COVID-19 crisis: Students’ engagement. In Education and Information Technologies (Vol. 26, Issue 6, pp. 7033–7055). Springer. https://doi.org/10.1007/s10639-021-10566-4

Kujansuu, E., & Tapio, T. (2004). Codewitz – An International Project for Better Programming Skills. Proceedings of ED-MEDIA 2004--World Conference on Educational Multimedia, Hypermedia & Telecommunications, 2237–2239.

Mann, S., & Robinson, A. (2009). Boredom in the lecture theatre: An investigation into the contributors, moderators and outcomes of boredom amongst university students. British Educational Research Journal, 35(2), 243–258. https://doi.org/10.1080/01411920802042911

Medvediev, M. (2019). The use of E-olymp internet portal in programming competitions. Olympiads Inf, 13, 201-208.

Owolabi, J., Olanipekun, P., & Iwerima, J. (2014). Mathematics Ability and Anxiety, Computer and Programming Anxieties, Age and Gender as Determinants of Achievement in Basic Programming. GSTF Journal on Computing (JoC), 3(4), 109–114. https://doi.org/10.7603/s40601-013-0047-4

Rooney, D., & Nyström, S. (2018). Simulation: A complex pedagogical space. In Australasian Journal of Educational Technology (Issue 6).

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. In Houghton Mifflin Company.

Staubitz, T., Klement, H., Teusner, R., Renz, J., & Meinel, C. (2016, April). CodeOcean-A versatile platform for practical programming excercises in online environments. In 2016 IEEE Global Engineering Education Conference (EDUCON) (pp. 314-323). IEEE.

Umar, I. N., & Hui, T. H. (2012). Learning Style, Metaphor and Pair Programming: Do they Influence Performance? Procedia - Social and Behavioral Sciences, 46, 5603–5609. https://doi.org/10.1016/j.sbspro.2012.06.482

Zinovieva, I. S., Artemchuk, V. O., Iatsyshyn, A. V., Popov, O. O., Kovach, V. O., Iatsyshyn, A. V., ... & Radchenko, O. V. (2021, March). The use of online coding platforms as additional distance tools in programming education. In Journal of physics: Conference series (Vol. 1840, No. 1, p. 012029). IOP Publishing.

Published

2024-12-28

How to Cite

Putri, T. T. A., Wan Yahaya, W. A. J., Mohamed Mokmin, N. A., Sriadhi, S., & Alie, M. F. (2024). The effects of machine-learning programming simulator on university students’ engagement in learning programming: Kesan simulator pengaturcaraan pembelajaran mesin terhadap penglibatan pelajar universiti. ATTARBAWIY: Malaysian Online Journal of Education, 8(2), 39–46. https://doi.org/10.53840/attarbawiy.v8i2.228