Hybrid data analytic technique for grading fairness

Purpose Fair grading produces learning ability levels that are understandable and acceptable to both learners and instructors. Norm-referenced grading can be achieved by several means such as z score, K -means and a heuristic. However, these methods typically deliver the varied degrees of grading fairness depending on input score data. Design/methodology/approach To attain the fairest grading, this paper proposes a hybrid algorithm that integrates z score, K -means and heuristic methods with a novel fairness objective function as a decision function. Findings Depending on an experimented data set, each of the algorithm's constituent methods could deliver the fairest grading results with fairness degrees ranging from 0.110 to 0.646. We also pointed out key factors in the fairness improvement of norm-referenced achievement grading. Originality/value The main contributions of this paper are four folds: the definition of fair norm-referenced grading requirements, a hybrid algorithm for fair norm-referenced grading, a fairness metric for norm-referenced grading and the fairness performance results of the statistical, heuristic and machine learning methods.