Hybrid data analytic technique for grading fairness
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 20 April 2022
Issue publication date: 17 March 2023
Abstract
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.
Keywords
Acknowledgements
This work is financially supported by the Department of Computer Science, Faculty of Science, Kasetsart University, Thailand.
Data Availability: The data used to support the findings of the study are included in the article.
Conflicts of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper.
Citation
Banditwattanawong, T., Jankasem, A.M.P. and Masdisornchote, M. (2023), "Hybrid data analytic technique for grading fairness", Data Technologies and Applications, Vol. 57 No. 1, pp. 18-31. https://doi.org/10.1108/DTA-01-2022-0047
Publisher
:Emerald Publishing Limited
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