What Can We Learn from Exam Grade Distributions?

A course with good learning outcomes is one in which most of the enrolled students achieve the mastery specified in the predefined learning objectives. Since the enrolment is invariably a mix of students with heterogeneous capabilities, the class average grade is a poor indicator of how the class is...

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Autor principal: Daniel Lewin
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Lenguaje:EN
Publicado: Georgia Southern University 2021
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Acceso en línea:https://doaj.org/article/47598e3dacec4950a73ac0a87bdb3e94
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spelling oai:doaj.org-article:47598e3dacec4950a73ac0a87bdb3e942021-12-02T18:48:36ZWhat Can We Learn from Exam Grade Distributions?1931-474410.20429/ijsotl.2021.150207https://doaj.org/article/47598e3dacec4950a73ac0a87bdb3e942021-11-01T00:00:00Zhttps://digitalcommons.georgiasouthern.edu/ij-sotl/vol15/iss2/7https://doaj.org/toc/1931-4744A course with good learning outcomes is one in which most of the enrolled students achieve the mastery specified in the predefined learning objectives. Since the enrolment is invariably a mix of students with heterogeneous capabilities, the class average grade is a poor indicator of how the class is divided into at least two groups, of high- and low-performers. Clearly, achieving the desired outcomes implies increasing the proportion of high-performing students and their mean grade by providing appropriately designed teaching protocols. In this paper, the actual class grade distribution is approximated by a bimodal probability distribution function, whose parameters enable the proportions and average performance of these two groups to be quantified. This paper describes the methodology to achieve this and demonstrates its usage to diagnose example exam grade distributions, as well as to provide quantification of the impact of pedagogic changes on the degree of achieving teaching objectives.Daniel LewinGeorgia Southern Universityarticleassessmentbimodal distributionsoutcomesTheory and practice of educationLB5-3640ENInternational Journal for the Scholarship of Teaching and Learning, Vol 15, Iss 2 (2021)
institution DOAJ
collection DOAJ
language EN
topic assessment
bimodal distributions
outcomes
Theory and practice of education
LB5-3640
spellingShingle assessment
bimodal distributions
outcomes
Theory and practice of education
LB5-3640
Daniel Lewin
What Can We Learn from Exam Grade Distributions?
description A course with good learning outcomes is one in which most of the enrolled students achieve the mastery specified in the predefined learning objectives. Since the enrolment is invariably a mix of students with heterogeneous capabilities, the class average grade is a poor indicator of how the class is divided into at least two groups, of high- and low-performers. Clearly, achieving the desired outcomes implies increasing the proportion of high-performing students and their mean grade by providing appropriately designed teaching protocols. In this paper, the actual class grade distribution is approximated by a bimodal probability distribution function, whose parameters enable the proportions and average performance of these two groups to be quantified. This paper describes the methodology to achieve this and demonstrates its usage to diagnose example exam grade distributions, as well as to provide quantification of the impact of pedagogic changes on the degree of achieving teaching objectives.
format article
author Daniel Lewin
author_facet Daniel Lewin
author_sort Daniel Lewin
title What Can We Learn from Exam Grade Distributions?
title_short What Can We Learn from Exam Grade Distributions?
title_full What Can We Learn from Exam Grade Distributions?
title_fullStr What Can We Learn from Exam Grade Distributions?
title_full_unstemmed What Can We Learn from Exam Grade Distributions?
title_sort what can we learn from exam grade distributions?
publisher Georgia Southern University
publishDate 2021
url https://doaj.org/article/47598e3dacec4950a73ac0a87bdb3e94
work_keys_str_mv AT daniellewin whatcanwelearnfromexamgradedistributions
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