Norm-Referenced Achievement Grading: Methods and Comparison

Banditwattanawong, Thepparit; Masdisornchote, Masawee

doi:10.1007/978-3-030-58669-0_14

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…is paper significantly extends our immature work [10] with a full-fledged algorithm, a newly practical data set, a newly experimented machine learning method, a set of new findings, and a novel guideline for method selection.…”

Section: Related Workmentioning

confidence: 82%

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

Banditwattanawong

Masdisornchote

2021

Applied Computational Intelligence and Soft Computing

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Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.

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Section: Related Workmentioning

confidence: 82%

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

Banditwattanawong

Masdisornchote

2021

Applied Computational Intelligence and Soft Computing

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“…Bai and Chen (2006) automatically constructed grade membership functions for fuzzy reasoning to infer student scores. Our prior works (Banditwattanawong and Masdisornchote, 2020a, b, 2021) proposed a heuristic for norm-referenced grading and demonstrated for the first time how to apply K -means and partitioning around medoids (PAM) (Kaufmann and Rousseeuw, 1987) to accomplish such a purpose. The grading result qualities of the heuristic, z score, K -means and PAM were comparatively quantified by using the Davies-Bouldin index, which is not suitable for grading fairness evaluation.…”

Section: Related Workmentioning

confidence: 99%

Hybrid data analytic technique for grading fairness

Banditwattanawong

Jankasem²,

Masdisornchote

2022

DTA

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PurposeFair 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/approachTo 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.FindingsDepending 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/valueThe 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.

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Norm-Referenced Achievement Grading: Methods and Comparison

Cited by 2 publications

References 7 publications

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

Hybrid data analytic technique for grading fairness

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