CoChat
Sign Up
Cited by 13

Instructors' perceptions of intelligent tutoring systems and their implications for studying computer programming in Omani higher education institutions

Al-Shanfari, Lamiya, Abdullah, Shubair, Fstnassi, Tarek, Al-Kharusi, Sultan·2023·International Journal of Membrane Science and Technology
DOI 10.15379/ijmst.v10i2.1395
Open this paper in CoChat — free account

Abstract

It is a serious problem that universities worldwide, especially in the middle east, have a high rate of failure in programming courses. To overcome this issue which could probably be due to students’ lack of engagement, motivation and problem-solving skills, the Intelligent Tutoring System (ITS) can be an effective tool for enhancing student learning. Despite its effectiveness, no prior research has been conducted on the impact of using ITS in higher education institutions in Oman. This study therefore investigates the perceptions of Omani University instructors to determine the levels of awareness, readiness and challenges among Omani higher institutions concerning their integration of ITS. Also, to investigate the reasons for the poor achievement of students in programming courses. We adopted two approaches in this study. First, quantitative research was conducted via a 5-point Likert scale questionnaire distributed to 59 programming instructors from different higher education institutions. Second, quantitative research was interviewing 10 instructors. Our questionnaire data reveal that most of the participants are aware of ITS’ effectiveness in the learning process. They believe that Oman’s higher education institutions are ready to adopt ITS if sufficient training and support is provided. Via text mining, our interview data illustrates the reasons behind poor performance in programming courses which includes English language barriers, deficient critical thinking, lack problem-solving skills and inadequate modes of teaching. This study clarifies the situation of higher educational institutions in term of their awareness, redness and the challenges that they may face in adopting ITS.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.15379/ijmst.v10i2.1395

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction; consequently, teaching and learning effects were somewhat improved.

    “Applying AI technologies can facilitate analyzing students' learning procedures, performance, and needs, providing instructors with timely feedback, and assisting them in delivering adaptive instruction.”

    From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Al-Shanfari et al., 2023

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Most instructors in Omani universities considered intelligent tutoring systems effective in providing customized instruction.

    “They found that most participants considered ITS effective in providing customized instruction; however, the lack of support and guidance in using ITS brought the instructors substantial challenges.”

    From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Al-Shanfari et al. (2023)

  • Lack of support and guidance in using intelligent tutoring systems brought instructors substantial challenges.

    “They found that most participants considered ITS effective in providing customized instruction; however, the lack of support and guidance in using ITS brought the instructors substantial challenges.”

    From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Al-Shanfari et al. (2023)

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 29

  1. Nwana H. Intelligent tutoring systems: an overview. Artif Intell Rev. 1990;4(4):251-277.
  2. Bull S, Kay J. SMILI: A Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields. Int J Artif Intell Educ. 2016;26(1):293-331.
  3. Kay J, Li L, Fekete A. Learner Reflection in Student Self-assessment. In: Proceeding of the Ninth Australasian Computing Education Conference (ACE2007), Ballarat, Australia. CRPIT, 66. ACS; 2007. p. 89-95.
  4. Price TW, Dong Y, Lipovac D. ISnap: Towards intelligent tutoring in novice programming environments. In: Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE. 2017. p. 483-488.
  5. Mitrovic A, Martin B. Evaluating the effect of open student models on self-assessment. Int J Artif Intell Educ. 2007;17(2):121-144.
  6. Rivers K, Koedinger KR. Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor. Int J Artif Intell Educ. 2017;27(1):37-64.
  7. Phillips A, Pane JF, Reumann-Moore R, Shenbanjo O. Implementing an adaptive intelligent tutoring system as an instructional supplement. Educ Technol Res Dev. 2020;68(3):1409-1437.
  8. Abu Ghali MJ, Abu Ayyad A, Abu-Naser SS, Abu Laban M. An intelligent tutoring system for teaching English grammar. Int J Acad Eng Res IJAER. 2018;2(2):1-6.
  9. Malik SI, Coldwell-Neilson J. Comparison of traditional and ADRI based teaching approaches in an introductory programming course. J Inf Technol Educ Res. 2017;16(1):267-283.
  10. Alturki RA. Measuring and improving student performance in an introductory programming course. Informatics in Education. 2016;15(2):183-204.
  11. Cheah CS. Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemp Educ Technol. 2020;12(2):1-14.
  12. Hwang G-J, Xie H, Wah BW, Gašević D. Vision, challenges, roles and research issues of Artificial Intelligence in Education. Comput Educ Artif Intell. 2020;1:100001.
  13. Moreno-Guerrero A-J, López-Belmonte J, Marín-Marín J-A, Soler-Costa R. Scientific development of educational artificial intelligence in Web of Science. Future Internet. 2020;12(8):124.
  14. Simon Luxton-Reilly A, Ajanovski Vv, Fouh E, Gonsalvez C, Leinonen J, Parkinson J, Poole M, Thota N. Pass rates in introductory programming and in other STEM disciplines. In: Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education. 2019. p. 53-71.
  15. Watson C, Li FWB. Failure rates in introductory programming revisited. In: Proceedings of the 2014 conference on Innovation technology in computer science education -ITiCSE '14. 2014. p. 39-44.
  16. Shi YQ, Huang S, Zheng CY. Research on programming courses teaching based on blended learning. In: Proceedings of the 5th International Conference on Frontiers of Educational Technologies, Beijing, China. 2019. p. 30-34.
  17. Mohoroviˇci ć S, Tijan E. Using screencasts in computer programming courses. In: Proceedings of the 22nd EAEEIE Annual Conference. 2011. p. 220-225.
  18. Alakeel A. Investigating Difficulties of Learning Computer Programming in Saudi Arabia. Univ J Educ Res. 2015;3(9):567-577.
  19. F. C. . Villacorta, Jr and A. A. . Arnado, "Competencies, Instructional Skills, and Challenges of Teachers in Implementing the Technical- Vocational and Livelihood Senior High School Track", International Journal of Membrane Science and Technology, vol. 10, no. 2, pp. 653-678, 2023.
  20. Tawafak RM, Romli A, Malik SI, Shakir M, al Farsi G. A systematic review of personalized learning: Comparison between e-learning and learning by coursework program in Oman. Int J Emerging Technol Learn. 2019;14(9):93-104.
  21. Islam Sallam and Montasser Abdelghani, "Systemic approach to Smart Urban Transformations Of Cities Case of Muscat-Oman", International Journal of Membrane Science and Technology, vol. 10, no. 2, pp. 630-652, 2023.
  22. Creswell J, Creswell J. DESIGNING RESEARCH. In: Research design: Qualitative, quantitative, and mixed methods approaches. 5th ed. SAGE Publications, Inc.; 2017.
  23. Venkatesh V, Brown SA, Bala H. Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly. 2013. p. 21-54.
  24. Allen IE, Seaman CA. Likert scales and data analyses. Qual Prog. 2007;40(7):64-65.
  25. Sekaran U, Bougie R. Research methods for business: A skill building approach. John Wiley & Sons; 2016.
  26. Siegel S, Castellan NJ Jr. In Nonparametric Statistics for the Behavioral Sciences. 2nd ed. McGraw-Hill; 2002. p. 25-26.
  27. Manning CD, Schütze H. Foundations of statistical natural language processing. MIT Press; 1999.
  28. Agogo D, Hess TJ. Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research. In: Deokar A, Gupta A, Iyer L, Jones M (eds) Analytics and Data Science. Annals of Information Systems. Springer; 2018. p. 163-178.
  29. Zhang J, Yan J, Chen K, Gao C. N-gram MalGAN: Evading machine learning detection via feature n-gram. Digital Commun Networks. 2022;8(4):485-491. DOI: https://doi.org/10.15379/ijmst.v10i2.1395 This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

Cited by

Sorted by citations

Authors
Al-Shanfari, L., Abdullah, S., Fstnassi, T., & Al-Kharusi, S.
Venue
International Journal of Membrane Science and Technology
Publisher
Cosmos Scholars Publishing House
Published
2023-06-21
Volume
10
Issue
2
Pages
947-967
ISSN
2410-1869
DOI
10.15379/ijmst.v10i2.1395
Cited by
13
References
0

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review