International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 18, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1579KB)
Patterns and Predictors of Student Technological Proficiency in Heis: A Validated Instrument and Machine Learning Analysis
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Author(s)
Abdelilah Chahid
1,*
Youssef El Marzak
1
Ossama Aouane
2
Khalifa Mansouri
1
Index Terms
Digital Competence, AI Literacy, Machine Learning, Higher Education, Feature Selection, Support Vector Classifier, Gaussian Naïve Bayes
Abstract
This study examines AI-related technological proficiency among undergraduate students at the University of Casablanca and identifies the most informative indicators for prediction. Using a validated 30-item instrument covering AI applications, AI-related skills, and improvement strategies, data were collected from 600 students drawn from science and humanities programs. Overall proficiency was moderate: 63.3% of respondents met the predefined threshold, and significant group differences were observed by gender and academic specialization. For predictive modeling, correlation-based feature selection retained 17 high-value items. Two classifiers were then trained and evaluated using a 75/25 hold-out split, complemented by repeated stratified 10-fold cross-validation to assess stability. The Support Vector Classifier achieved 96.7% test accuracy with AUROC = 0.9666, while Gaussian Naïve Bayes reached 94.7% accuracy with AUROC = 0.9560; cross-validated estimates remained consistent with these results, supporting robustness. These findings indicate that a reduced set of questionnaire items can provide reliable estimates of students’ AI-related technological proficiency and can support scalable assessment and targeted interventions in higher education.
Cite This Paper
Abdelilah Chahid, Youssef El Marzak, Ossama Aouane, Khalifa Mansouri, "Patterns and Predictors of Student Technological Proficiency in Heis: A Validated Instrument and Machine Learning Analysis", International Journal of Modern Education and Computer Science(IJMECS), Vol.18, No.3, pp. 90-105, 2026. DOI:10.5815/ijmecs.2026.03.06
Reference
[1]Digital competence in higher education research: A systematic literature review Zhao, Y., Pinto Llorente, A. M., & Sánchez Gómez, M. C. (2021). Computers & Education, 168, Article 104212. https://doi.org/10.1016/j.compedu.2021.104212
[2]The digital competence of academics in higher education: is the glass half empty or half full? Inamorato dos Santos, A., Chinkes, E., Carvalho, M. A. G., Solórzano, C. M. V., & Marroni, L. S. (2023). International Journal of Educational Technology in Higher Education, 20, Article 9. https://doi.org/10.1186/s41239-022-00376-0
[3]Digital competence in student learning with Generative Artificial Intelligence: Policy Implications from World Class Universities Zhang, Y., & Tian, Z. (2025). Journal of University Teaching & Learning Practice (à paraître / open-publishing). https://doi.org/10.53761/av7c8830
[4]Ranieri, M., Biagini, G., & Cuomo, S. (2025). AI Literacy in Higher Education: A Systematic Approach to Questionnaire Development and Validation. International Journal of Digital Literacy & Digital Competence, 16(1). https://doi.org/10.4018/IJDLDC.388469
[5]Cabero-Almenara, J., Gutiérrez-Castillo, J. J., Guillén-Gámez, F. D., & Gaete-Bravo, A. F. (2023). Digital competence of higher education students as a predictor of academic success. International Journal of Educational Technology in Higher Education, 20:9. https://doi.org/10.1186/s41239-022-00376-0
[6]CHAHID, Abdelilah, AHRIZ, Souad, EL GUEMMAT, Kamal, et al. Digital Transformation in Higher Education Obstacle Assessment and Development of Strategies against Cybersecurity Threats: The Case of Moroccan Universities. Engineering, Technology & Applied Science Research, 2025, vol. 15, no 1, p. 19809-19815.
[7]Mena-Guacas, A. F., Meza-Morales, J. A., Fernández, E., & López-Meneses, E. (2024). Digital Collaboration in Higher Education: A Study of Digital Skills and Collaborative Attitudes in Students from Diverse Universities. Education Sciences, 14(1), 36. https://doi.org/10.3390/educsci14010036
[8]ØVRELID, Egil. Exploring the alignment between digital strategies and educational practices in higher education infrastructures. Education Sciences, 2022, vol. 12, no 10, p. 711. https://doi.org/10.3390/educsci12100711
[9]NKANSAH, Jacob Oppong et OLDAC, Yusuf Ikbal. Unraveling the attributions of digital literacy skills and knowledge gap in Ghana’s higher education: Undergraduate students voices in a phenomenological study. Education and Information Technologies, 2024, vol. 29, no 12, p. https://doi.org/10.1007/s10639-024-12483-8
[10]INAMORATO DOS SANTOS, Andreia, CHINKES, Ernesto, CARVALHO, Marco AG, et al. The digital competence of academics in higher education: is the glass half empty or half full?. International journal of educational technology in higher education, 2023, vol. 20, no 1, p. 9. https://doi.org/10.1186/s41239-022-00376-0
[11]CROMPTON, Helen et BURKE, Diane. Artificial intelligence in higher education: the state of the field. International journal of educational technology in higher education, 2023, vol. 20, no 1, p. 22. https://doi.org/10.1186/s41239-023-00392-8
[12]ZHANG, Youliang et TIAN, Zhen. Digital competencies in student learning with generative artificial intelligence: Policy implications from world-class universities. Journal of University Teaching and Learning Practice, 2025, vol. 22, no 2, p. 1-22. https://doi.org/ 10.53761/av7c8830
[13]Alhothali, A. (2022). Predicting Student Outcomes in Online Courses Using Machine Learning Models. Sustainability, 14(10), Article 6199. https://doi.org/10.3390/su14106199
[14]MOHAMED NAFURI, Ahmad Fikri, SANI, Nor Samsiah, ZAINUDIN, Nur Fatin Aqilah, et al. Clustering analysis for classifying student academic performance in higher education. Applied Sciences, 2022, vol. 12, no 19, p. 9467. ; https://doi.org/10.3390/app12199467
[15]Xue, Y., Chinapah, V., & Zhu, C. (2025). A Comparative Analysis of AI Privacy Concerns in Higher Education: News Coverage in China and Western Countries. Educ. Sci., 15(6), 650. https://doi.org/10.3390/educsci15060650
[16]Amirgaliyev, B., Mussabek, M., Rakhimzhanova, T., & Zhumadillayeva, A. (2025). A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face Recognition with Applications. Sensors, 25(5), 1410. https://doi.org/10.3390/s25051410
[17]Kazimova, D. (2025). Evaluating the impact of pattern recognition on AI skills in universities. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1592209
[18]Marín-Jiménez, Á., & Vázquez-Camacho, P. (2023). Exploring the opportunities and challenges of NLP models in higher education. Frontiers in Education, 8, Article 1166682. https://doi.org/10.3389/feduc.2023.1166682
[19]Phokoye, S. P., Epizitone, A., Nkomo, N., Mthalane, P. P., Moyane, S. P., Khumalo, M. M., Luthuli, M., & Zondi, N. P. (2024). Exploring the Adoption of Robotics in Teaching and Learning in Higher Education Institutions. Informatics, 11(4), 91. https://doi.org/10.3390/informatics11040091
[20]Yang, G., Lv, H., Chen, C., & Zhang, Y. (2022). Intelligent robotics for healthcare: Recent advances and future outlook. IEEE Reviews in Biomedical Engineering, 1 https://doi.org/10.1109/ISTTCA53489.2021.9654781
[21]MAJEED, Ban Hassan. The skill of making a decision and its relationship of academic achievement among students. 2021.https://doi.org/10.3991/ijes.v9i4.26363
[22]ABOUSABER, Inam. Digital Competencies of Saudi University Graduates Towards Digital Society: The Case of The University of Tabuk. Advances in Science, Technology and Engineering Systems Journal Vol. 7, No. 2, 171-178 (2022) DOI: 10.25046/aj070217
[23]Dringó-Horváth, I., Rajki, Z., & Nagy, J. T. (2025). University teachers’ digital competence and AI literacy: Moderating role of gender, age, experience, and discipline. Education Sciences, 15(7), 868. https://doi.org/10.3390/educsci15070868
[24]AKANBI, Olawale Basheer. Application of naive bayes to students’ performance classification. Asian journal of probability and statistics, 2023, vol. 25, no 1, p. 35-47. https://doi.org/10.9734/AJPAS/2023/v25i1536
[25]AHMED, Esmael. Student performance prediction using machine learning algorithms. Applied computational intelligence and soft computing, 2024, vol. 2024, no 1, p. 4067721. https://doi.org/10.1155/2024/4067721
[26]WASEEM, Quadri, DIN, Wan Isni Sofiah Binti Wan, RAHMAN, Azamuddin Bin Ab, et al. A survey and analysis of feature selection techniques in machine learning for IoT device classification within smart buildings. Innovative Infrastructure Solutions, 2025, vol. 10, no 9, p. 407. https://doi.org/10.1007/s41062-025-02203-7
[27]PUDJIHARTONO, Nicholas, FADASON, Tayaza, KEMPA-LIEHR, Andreas W., et al. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in bioinformatics, 2022, vol. 2, p. 927312.https://doi.org doi/10.3389/fbinf.2022.92731