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Reducing the Impact of Bias in Oral Assessments

Gerlt, Michael LU orcid ; von Platten, Jenny LU ; Klöckner, Maike LU ; Larsson, Viktor LU and Cedervall, Tommy LU (2023) 2023.
Abstract
Oral assessment is an important method to evaluate the learning outcomes of scientific courses. However,there are certain limitations when oral assessment is applied.One of these limitations, which is not apparent in anonymous written exams, is the existence of biases which could lead to unfair (positive or negative) outcomes of the assessment. This could lead to decreased motivation and sense of belonging among minority student groups, potentially upholding or even increasing inequalities. The major issue with biases is that most of them are unconscious, meaning that it is very tough to mitigate. In this manuscript, we analyze the emergence and effect of expectancy-based bias through personal construct theory in order to find approaches... (More)
Oral assessment is an important method to evaluate the learning outcomes of scientific courses. However,there are certain limitations when oral assessment is applied.One of these limitations, which is not apparent in anonymous written exams, is the existence of biases which could lead to unfair (positive or negative) outcomes of the assessment. This could lead to decreased motivation and sense of belonging among minority student groups, potentially upholding or even increasing inequalities. The major issue with biases is that most of them are unconscious, meaning that it is very tough to mitigate. In this manuscript, we analyze the emergence and effect of expectancy-based bias through personal construct theory in order to find approaches to reduce the influence of bias in oral assessment. As such, we address biases existing before interaction with the student (stereotype), emerging from interaction with the student (halo bias), and how these can contribute to a biased idea of the student in the evaluator’s mind. We finally discuss how this can cause cognitive dissonance and biased assessment when student performance is not in line with the teacher's cognitive model of the student and propose solutions on how to deal with this to minimize bias in oral assessment. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
LTH:s 12:e Pedagogiska Inspirationskonferens, 7 december 2023
volume
2023
pages
4 pages
ISSN
2003-3761
language
English
LU publication?
yes
id
29d9063d-e5fd-4e47-bf43-407741ea3885
alternative location
https://www.lth.se/fileadmin/cee/genombrottet/konferens2023/D5b_Gerlt_etal.pdf
date added to LUP
2023-12-18 13:27:00
date last changed
2024-05-15 11:42:11
@inproceedings{29d9063d-e5fd-4e47-bf43-407741ea3885,
  abstract     = {{Oral assessment is an important method to evaluate the learning outcomes of scientific courses. However,there are certain limitations when oral assessment is applied.One of these limitations, which is not apparent in anonymous written exams, is the existence of biases which could lead to unfair (positive or negative) outcomes of the assessment. This could lead to decreased motivation and sense of belonging among minority student groups, potentially upholding or even increasing inequalities. The major issue with biases is that most of them are unconscious, meaning that it is very tough to mitigate. In this manuscript, we analyze the emergence and effect of expectancy-based bias through personal construct theory in order to find approaches to reduce the influence of bias in oral assessment. As such, we address biases existing before interaction with the student (stereotype), emerging from interaction with the student (halo bias), and how these can contribute to a biased idea of the student in the evaluator’s mind. We finally discuss how this can cause cognitive dissonance and biased assessment when student performance is not in line with the teacher's cognitive model of the student and propose solutions on how to deal with this to minimize bias in oral assessment.}},
  author       = {{Gerlt, Michael and von Platten, Jenny and Klöckner, Maike and Larsson, Viktor and Cedervall, Tommy}},
  booktitle    = {{LTH:s 12:e Pedagogiska Inspirationskonferens, 7 december 2023}},
  issn         = {{2003-3761}},
  language     = {{eng}},
  month        = {{12}},
  title        = {{Reducing the Impact of Bias in Oral Assessments}},
  url          = {{https://lup.lub.lu.se/search/files/166982979/Reducing_the_Impact_of_Oral_Assessments_proceedings.pdf}},
  volume       = {{2023}},
  year         = {{2023}},
}