Influence of individual versus collaborative peer assessment on score accuracy and learning outcomes in higher education: an empirical study

Abstract

Maximising the accuracy and learning of self and peer assessment activities in higher education requires instructors to make several design decisions, including whether the assessment process should be individual or collaborative, and, if collaborative, determining the number of members of each peer assessment team. In order to support this decision, a quasi-experiment was carried out in which 82 first-year students used three peer assessment modalities. A total of 1574 assessments were obtained. The accuracy of both the students’ self-assessment and their peer assessment was measured. Results show that students’ self-assessment significantly improved when groups of three were used, provided that those with the 20% poorest performances were excluded from the analysis. This suggests that collaborative peer assessment improves learning. Peer assessment scores were more accurate than self-assessment, regardless of the modality, and the accuracy improved with the number of assessments received. Instructors need to consider the trade-off between students’ improved understanding, which favours peer assessment using groups of three, and a higher number of assessments, which, under time constraints, favours individual peer assessment.

Keywords:

• Experimental research
• student assessment
• self-assessment
• peer assessment
• collaborative evaluation

Acknowledgement

We would like to thank the students who participated in our study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributor

Juan Ramón Rico-Juan received the Ph.D. degree in computer engineering from the University of Alicante. He is currently a senior Lecturer with University of Alicante and a researcher with pattern recognition and artificial intelligence group. His research interests include areas such as pattern recognition and machine learning, as well as working on structured data learning, distance editing, prototype selection and generation, and deep neural networks.

Dr Cristina Cachero is a senior lecturer at the University of Alicante’s Department of IT Languages and Systems, where she is responsible for teaching various courses in the areas of Software Engineering and Programming. Dr Cachero’s main line of research is empirical software engineering and, within this, the evaluation of (a) notations, methods and techniques and (b) individual traits that may affect the performance and preferences of subjects regarding different types of tasks.

Hermenegilda Macià received the degree in Mathematics from the University of Valencia and the Ph.D. degree in Computer Engineering from the University of Castilla-La Mancha. She is currently an Associate Professor with the Department of Mathematics of the University of Castilla-La Mancha in the Computer Science School in Albacete, Spain.

Additional information

Funding

This work has been partially funded by the University of Alicante’s Redes-I3CE de investigación en docencia universitaria del Instituto de Ciencias de la Educación (REDES-I3CE-2019-4607 and REDES-I3CE-2020-5069), by the AICO/2020/143 project, funded by the GVA, by the Access@IoT project, funded by the Spanish Ministry of Science and Innovation (PID2019-111196RB-I00) and by the EduTech (609785-EPP-1-2019-1-ES-EPPKA2-CBHE-JP) and the SkoPS (2020-1-DE01-KA226HE-005772) projects, funded by the EU’s Erasmus + Programme, and by the UCLM group research grant with reference 2021-GRIN-30993.