Expert-Informed Interval Type-2 Fuzzy Logic System for the Early Prediction of Support Needs and Failure Risk in Student Group Projects

Group projects are considered a fundamental component of higher education, as they enhance students’ 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’ 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’ 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–output 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’s effectiveness in handling linguistic and numerical uncertainties in supervisors’ evaluations of students’ required early interventions.