{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:07:12Z","timestamp":1780078032838,"version":"3.54.0"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Group projects are considered a fundamental component of higher education, as they enhance students\u2019 competencies and problem-solving abilities within professional learning environments. Therefore, ensuring student success and providing effective supervision is essential. However, this remains a challenging task due to the reliance on supervisors\u2019 expertise and the diverse characteristics and backgrounds of student groups. In this paper, we introduce a novel theoretical and practical interval type-2 fuzzy logic system (IT2FLS) for early prediction and guidance for novice supervisors by correlating and learning expert supervisors\u2019 assessments according to the required level of support and the risk of failure for student groups needing early intervention. Experimental evaluation was performed based on assessments of 33 graduation projects conducted by expert supervisors, which served as the input\u2013output data for developing interpretable white-box models that allow both novice and expert supervisors to transparently analyse reasoning processes and outcomes. The results demonstrate that the developed IT2FLS predicts the required level of support and the risk of failure for student groups with lower average error and standard deviation, outperforming the encountered Type-1 fuzzy logic systems. This study thus indicates the IT2FLS\u2019s effectiveness in handling linguistic and numerical uncertainties in supervisors\u2019 evaluations of students\u2019 required early interventions.<\/jats:p>","DOI":"10.3390\/computers15060347","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:36:18Z","timestamp":1780076178000},"page":"347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Expert-Informed Interval Type-2 Fuzzy Logic System for the Early Prediction of Support Needs and Failure Risk in Student Group Projects"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0813-142X","authenticated-orcid":false,"given":"Khalid","family":"Almohammadi","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Tabuk, Tabuk 71491, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Gupta, S., Page, F., Agg, C., Spivey, A.C., Hazeri, K., Zou, Y., Zhao, C., Lucas, C., and Childs, P.R.N. 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