{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:47:07Z","timestamp":1772556427146,"version":"3.50.1"},"reference-count":35,"publisher":"International Association of Online Engineering (IAOE)","issue":"1","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Eng. Ped."],"abstract":"<jats:p>Assessing competencies in engineering education increasingly requires digital assessment approaches that support learning regulation, instructional decision-making, and educational quality, rather than focusing solely on measurement efficiency. Computerized adaptive testing (CAT), grounded in Item Response Theory (IRT), provides a robust methodological foundation for personalized assessment. However, its pedagogical effectiveness in formative contexts depends critically on curriculum alignment, diagnostic capacity, and adaptive control strategies. This study proposes and evaluates a formative adaptive assessment framework for engineering education that integrates an IRT-based CAT engine with a Bayesian network\u2013 based diagnostic component. The framework is designed to support competency-oriented feedback, learning monitoring, and instructional interpretation within a curriculum-aligned assessment structure. Assessment relies on dichotomous multiple-choice items explicitly aligned with engineering learning outcomes, while item selection dynamically adapts to learners\u2019 evolving proficiency estimates. In parallel, probabilistic diagnostic modelling prioritizes under-assessed competencies throughout the adaptive process. Item calibration was conducted using empirical data collected from 612 university students in computer science, and system performance was examined through a simulation-based evaluation involving 500 simulated learners. Results demonstrate high estimation accuracy (r = 0.912) and satisfactory reliability for formative use across most learner profiles. Reduced precision at the extremes of the proficiency continuum and imbalances in item exposure were also observed, highlighting structural limitations primarily related to item bank coverage and curriculum representation rather than to the adaptive algorithms themselves. Overall, the proposed framework positions adaptive assessment as a pedagogically grounded tool for formative learning support, instructional decision-making, and quality assurance in engineering education.<\/jats:p>","DOI":"10.3991\/ijep.v16i1.60479","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:33:22Z","timestamp":1772548402000},"source":"Crossref","is-referenced-by-count":0,"title":["Designing Formative Adaptive Assessment for Engineering Education"],"prefix":"10.3991","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5643-7437","authenticated-orcid":false,"given":"Mohamed","family":"El Msayer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1652-8470","authenticated-orcid":false,"given":"Bouchra","family":"Bouihi","sequence":"additional","affiliation":[]},{"given":"Abdelmajid","family":"Bousselham","sequence":"additional","affiliation":[]},{"given":"Essaadia","family":"Aoula","sequence":"additional","affiliation":[]},{"given":"Adel","family":"Deraoui","sequence":"additional","affiliation":[]}],"member":"2371","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"39113","doi-asserted-by":"crossref","unstructured":"[1] Y. 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Songmuang, \u00ab Computerized Adaptive Testing Based on Decision Tree \u00bb, 2010 10th IEEE International Conference on Advanced Learning Technologies, p. 191\u2011193, juill. 2010, doi: 10.1109\/ICALT.2010.58.","DOI":"10.1109\/ICALT.2010.58"},{"key":"39157","doi-asserted-by":"crossref","unstructured":"[23] M. Imran, N. Almusharraf, M. S. Abdellatif, et M. Y. Abbasova, \u00ab Artificial Intelligence in Higher Education: Enhancing Learning Systems and Transforming Educational Paradigms \u00bb, International Journal of Interactive Mobile Technologies (iJIM), vol. 18, no 18, p. 34\u201148, sept. 2024, doi: 10.3991\/ijim.v18i18.49143.","DOI":"10.3991\/ijim.v18i18.49143"},{"key":"39159","doi-asserted-by":"crossref","unstructured":"[24] Y. Choi et C. 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Deckarm, et S. Strickroth, \u00ab AI-Enhanced Auto-Correction of Programming Exercises: How Effective is GPT-3.5? \u00bb, International Journal of Engineering Pedagogy (iJEP), vol. 13, no 8, p. 67\u201183, d\u00e9c. 2023, doi: 10.3991\/ijep.v13i8.45621.","DOI":"10.3991\/ijep.v13i8.45621"},{"key":"39181","doi-asserted-by":"crossref","unstructured":"[35] S. Lim et S. W. Choi, \u00ab Item exposure and utilization control methods for optimal test assembly \u00bb, Behaviormetrika, vol. 51, no 1, p. 125\u2011156, janv. 2024, doi: 10.1007\/s41237-023-00214-1.","DOI":"10.1007\/s41237-023-00214-1"}],"container-title":["International Journal of Engineering Pedagogy (iJEP)"],"original-title":[],"link":[{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/download\/60479\/17055","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/download\/60479\/17055","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:33:57Z","timestamp":1772548437000},"score":1,"resource":{"primary":{"URL":"https:\/\/online-journals.org\/index.php\/i-jep\/article\/view\/60479"}},"subtitle":["Integrating Computerized Adaptive Testing and Competency-Based Diagnostic Modelling"],"short-title":[],"issued":{"date-parts":[[2026,3,3]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,3]]}},"URL":"https:\/\/doi.org\/10.3991\/ijep.v16i1.60479","relation":{},"ISSN":["2192-4880"],"issn-type":[{"value":"2192-4880","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,3]]}}}