A novel possibilistic artificial immune-based classifier for course learning outcome enhancement

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In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is difficult to determine the unique most effective remedial action to take. Consequently, each rubric instance will be labeled with uncertain remedial actions which are elicited from quality assurance experts. Elements from possibility theory are used to (1) model the uncertainty about the most effective remedial action labeling each rubric instance and (2) adapt several parts of the standard AIRS algorithm in order to address the uncertainty in class labels. The performance of the proposed method is evaluated against an academic, university level, assessment dataset that has been built progressively over multiple academic semesters. Despite the uncertainty related to the class labels in the dataset, PAIRS3 showed a good performance in terms of accuracy level (close to 75%). Also, when compared to existing state-of-the-art possibilistic classifiers such as PAIRS2, non-specificity possibilistic decision trees (NSPDT), and cluster-based possibilistic decision trees (Clust-PDT), PAIRS3 achieved better accuracy improvement ranging from 10% (in case of Clust-PDT) to 17% (in case of PAIRS2).