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http://dx.doi.org/10.5391/JKIIS.2012.22.5.624

A Prediction Method of Learning Outcomes based on Regression Model for Effective Peer Review Learning  

Shin, Hyo-Joung (삼성전자)
Jung, Hye-Wuk (성균관대학교 컴퓨터공학과)
Cho, Kwang-Su (성균관대학교 인터렉션사이언스학과)
Lee, Jee-Hyoung (성균관대학교 컴퓨터공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.5, 2012 , pp. 624-630 More about this Journal
Abstract
The peer review learning is a method which improves learning outcome of students through feedback between students and the observation and analysis of other students. One of the important problems in a peer review system is to find proper evaluators to each learner considering characteristics of students for improving learning outcomes. Some of peer review systems randomly assign peer review evaluators to learners, or chose evaluators based on limited strategies. However, these systems have a problem that they do not consider various characteristics of learners and evaluators who participate in peer reviews. In this paper, we propose a novel prediction approach of learning outcomes to apply peer review systems considering various characteristics of learners and evaluators. The proposed approach extracts representative attributes from the profiles of students and predicts learning outcomes using various regression models. In order to verify how much outliers affect on the prediction of learning outcomes, we also apply several outlier removal methods to the regression models and compare the predictive performance of learning outcomes. The experiment result says that the SVR model which does not removes outliers shows an error rate of 0.47% on average and has the best predictive performance.
Keywords
Peer review; Regression model; SVR; Outlier; e-learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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