Understanding Learning Difficulties in Assembly Language among Mechanical Engineering Technology Students
Authors
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.1026EDU0460
Subject Category: Language
Volume/Issue: 10/26 | Page No: 6269-6283
Publication Timeline
Submitted: 2026-07-15
Accepted: 2026-07-20
Published: 2026-07-29
Abstract
Assembly language plays a fundamental role in microprocessor and embedded systems education by enabling students to understand how software instructions interact with processor registers, memory, status flags, and hardware-level operations. Despite its importance, the subject is often challenging for students outside computer science, particularly those in Mechanical Engineering Technology, because it requires reasoning at a much lower level of abstraction than is typical in engineering programming courses. This study examined the learning difficulties perceived by Mechanical Engineering Technology students when studying assembly language. A descriptive mixed-methods survey was administered to 30 undergraduate students enrolled in a microprocessor-related course. The instrument comprised Likert-scale items assessing perceived difficulty, confidence, and operational understanding, together with an open-ended question that invited students to describe the aspects of assembly language they found most difficult and explain their reasons. Descriptive findings indicated that students experienced the greatest difficulty in understanding data movement between registers and memory, followed by difficulties with addressing modes and the abstract nature of assembly instructions. Thematic analysis of the qualitative responses identified five recurring challenges: register-memory reasoning, addressing modes and data-source reasoning, execution tracing and prediction, low-level abstraction and visualisation, and confusion related to learning tools. These findings indicate that students' learning difficulties extend beyond syntax and memorisation, reflecting underdeveloped mental models of processor execution and a limited ability to visualise runtime behaviour. The study contributes empirical evidence from the context of engineering technology education and highlights the need for instructional approaches that explicitly support processor-state tracing, register-memory mapping, and progressive visualisation of addressing modes.
Keywords
assembly language, engineering technology education, learning difficulties, microprocessor education, STEM education
Downloads
References
1. Du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57-73. [Google Scholar] [Crossref]
2. Robins, A., Rountree, J., & Rountree, N. (2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13(2), 137-172. [Google Scholar] [Crossref]
3. Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 14-18. [Google Scholar] [Crossref]
4. Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., Simon, B., & Thomas, L. (2004). A multi-national study of reading and tracing skills in novice programmers. In Working group reports from ITiCSE on Innovation and Technology in Computer Science Education (ITiCSE-WGR ’04) (pp. 119-150). Association for Computing Machinery. [Google Scholar] [Crossref]
5. Sorva, J. (2013). Notional machines and introductory programming education. ACM Transactions on Computing Education, 13(2), Article 8, 1-31. [Google Scholar] [Crossref]
6. Sorva, J., Karavirta, V., & Malmi, L. (2013). A Review of Generic Program Visualization Systems for Introductory Programming Education. ACM Transactions on Computing Education, 13(4), Article 15, 1-64. [Google Scholar] [Crossref]
7. Ben-Ari, M. (2001). Constructivism in Computer Science Education. Journal of Computers in Mathematics and Science Teaching, 20(1), 45-73. [Google Scholar] [Crossref]
8. Qian, Y., & Lehman, J. (2017). Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review. ACM Transactions on Computing Education, 18(1), Article 1, 1-24. [Google Scholar] [Crossref]
9. Cheah, C. S. (2020). Factors Contributing to the Difficulties in Teaching and Learning of Computer Programming: A Literature Review. Contemporary Educational Technology, 12(2), ep272. [Google Scholar] [Crossref]
10. Meyer, J. H. F., & Land, R. (2003). Threshold concepts and troublesome knowledge: Linkages to ways of thinking and practising within the disciplines. In Improving Student Learning: Theory and Practice Ten Years On (pp. 412-424). Oxford Brookes University. [Google Scholar] [Crossref]
11. Meyer, J. H. F., & Land, R. (2005). Threshold concepts and troublesome knowledge (2): Epistemological considerations and a conceptual framework for teaching and learning. Higher Education, 49, 373-388. [Google Scholar] [Crossref]
12. Boustedt, J., Eckerdal, A., McCartney, R., Moström, J. E., Ratcliffe, M., Sanders, K., & Zander, C. (2007). Threshold concepts in computer science: Do they exist and are they useful? ACM SIGCSE Bulletin, 39(1), 504-508. [Google Scholar] [Crossref]
13. Rountree, J., & Rountree, N. (2009). Issues regarding threshold concepts in computer science. In Proceedings of the Eleventh Australasian Conference on Computing Education - Volume 95 (ACE ’09) (pp. 139-146). Australian Computer Society. [Google Scholar] [Crossref]
14. Eckerdal, A., McCartney, R., Moström, J. E., Ratcliffe, M., Sanders, K., & Zander, C. (2006). Putting threshold concepts into context in computer science education. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (pp. 103–107). Association for Computing Machinery. [Google Scholar] [Crossref]
15. Kyriakou, C., Gogoulou, A., & Grigoriadou, M. (2025). Main memory in program execution: Threshold concept in CS. SN Computer Science, 6, Article 464. [Google Scholar] [Crossref]
16. Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257-285. [Google Scholar] [Crossref]
17. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38(1), 1-4. [Google Scholar] [Crossref]
18. Mayer, R. E. (2005). Cognitive Theory of Multimedia Learning. In The Cambridge Handbook of Multimedia Learning (pp. 31-48). Cambridge University Press. [Google Scholar] [Crossref]
19. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. [Google Scholar] [Crossref]
20. Sullivan, G. M., & Artino, A. R. Jr. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541-542. [Google Scholar] [Crossref]
21. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Evaluating the Impacts of Mind Mapping Strategy on Developing EFL Students’ Critical Reading Skills
- Significance of Reading Instructions for Language Improvement in Children with Down Syndrome
- Prenasalised Consonants in Liangmai
- Metadiscourse Matters: Definitions, Models, and Advantages for ESL/ EFL Writing
- Blank Minds and Stuck Voices: Understanding and Addressing Cognitive Anxiety in High-Stakes ESL Speaking Tests