TY - GEN
T1 - Mitigating Generosity Bias in Peer Assessment
T2 - 24th IEEE International Conference on Advanced Learning Technologies, ICALT 2024
AU - Dery, Lihi
AU - Lange, Matan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In higher education courses, peer grading can keep students engaged during class presentations. Defining how students grade the work of their peers requires careful consideration. Two commonly used approaches exist. The first involves asking students to order the projects from best to worst. However, this approach imposes a high cognitive load on students, as ordering numerous projects is challenging. The second and most prevalent approach involves soliciting qualitative or numerical scores from students. However, a common challenge arises as students tend to be generous in the scores they assign their peers. Consequently, this leads to a situation where all projects receive similar, high grades. To address this challenge, we have developed a novel, interactive model. This model requires students to provide grades and to respond to a few pairwise comparison questions when necessary. The model was implemented as a mobile web application and tested in a university course, confirming its validity and efficiency.
AB - In higher education courses, peer grading can keep students engaged during class presentations. Defining how students grade the work of their peers requires careful consideration. Two commonly used approaches exist. The first involves asking students to order the projects from best to worst. However, this approach imposes a high cognitive load on students, as ordering numerous projects is challenging. The second and most prevalent approach involves soliciting qualitative or numerical scores from students. However, a common challenge arises as students tend to be generous in the scores they assign their peers. Consequently, this leads to a situation where all projects receive similar, high grades. To address this challenge, we have developed a novel, interactive model. This model requires students to provide grades and to respond to a few pairwise comparison questions when necessary. The model was implemented as a mobile web application and tested in a university course, confirming its validity and efficiency.
KW - Interactive learning
KW - learning technologies
KW - peer assessment
KW - peer evaluation
KW - peer grading
KW - preference elicitation
KW - technology-enhanced assessment
UR - http://www.scopus.com/inward/record.url?scp=85203826342&partnerID=8YFLogxK
U2 - 10.1109/ICALT61570.2024.00086
DO - 10.1109/ICALT61570.2024.00086
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AN - SCOPUS:85203826342
T3 - Proceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024
SP - 274
EP - 276
BT - Proceedings - 2024 IEEE International Conference on Advanced Learning Technologies, ICALT 2024
A2 - Altinay, Zehra
A2 - Chang, Maiga
A2 - Kuo, Rita
A2 - Tlili, Ahmed
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 July 2024 through 4 July 2024
ER -