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Machine learning for the peer assessment credibility


Lin, Y and Han, SC and Kang, BH, Machine learning for the peer assessment credibility, 23-27 April, Lyon, France, pp. 117-118. ISBN 9781450356404 (2018) [Conference Edited]

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DOI: doi:10.1145/3184558.3186957


The peer assessment approach is considered to be one of the best solutions for scaling both assessment and peer learning to global classrooms, such as MOOCs. However, some academic staff hesitate to use a peer assessment approach for their classes due to concerns about its credibility and reliability. The focus of our research is to detect the credibility level of each assessment performed by students during peer assessment. We found three major scopes in assessing the credibility level of evaluations, 1) Informativity, 2) Accuracy, and 3) Consistency. We collect assessments, including comments and grades provided by students during the peer assessment process and then each feedback-and-grade pair is labeled with its credibility level by Mechanical Turk evaluators. We extract relevant features from each labeled assessment and use them to build a classifier that attempts to automatically assess its level of credibility in C5.0 Decision Tree classifier. The evaluation results show that the model can be used to automatically classify peer assessments as credible or non-credible, with accuracy in the range of 88%.

Item Details

Item Type:Conference Edited
Keywords:peer assessment, educational data mining, credibility assessment
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Information retrieval and web search
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Lin, Y ( Yingru Lin)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:125747
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2018-05-03
Last Modified:2018-05-04

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