A Predictive Model to Evaluate Students' Cognitive Engagement in Online Learning

被引：31

作者：

Shukor, Nurbiha A. ^{[1
]}

Tasir, Zaidatun ^{[1
]}

Van der Meijden, Henny ^{[2
]}

Harun, Jamalludin ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Fac Educ, Skudai 81310, Johor, Malaysia

[2] Radboud Univ Nijmegen, NL-6525 ED Nijmegen, Netherlands

来源：

5TH WORLD CONFERENCE ON EDUCATIONAL SCIENCES | 2014年 / 116卷

关键词：

Online learning; cognitve engagement; content analysis; data mining;

D O I：

10.1016/j.sbspro.2014.01.1036

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

The expanding usage of online learning at all levels of education has drawn attention to the quality of online learning. In this study, online learning quality is evaluated through students' cognitive engagement which is reflected in their online written messages in discussions and their online participation. This study proposes the use of two types of data: students' participation, and written messages. Both types of data was collected and analyzed using the data mining technique to produce a predictive model that illustrates students' pathways while engaging in online learning cognitively. The findings of this study indicate that from 22 variables, only two were significant for students' online cognitive engagement; sharing information and posting high-level messages. The two variables led to the formation of three different pathways in the students' predictive model. (C) 2013 The Authors Published by Elsevier Ltd.

引用

页码：4844 / 4853

页数：10

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