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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7062
Title: Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts
Authors: Popescu, Elvira
Dascalu, Mihai
Becheru, Alex
Crossley, Scott
Trausan-Matu, Stefan
Keywords: social media
textual complexity analysis
student performance
learning style
Issue Date: 2016
Publisher: IEEE
Citation: Popescu, E., Dascalu, M., Becheru, A., Crossley, S. A., & Trausan-Matu, S. (2016). Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts – A Preliminary Study. In 16th IEEE Int. Conf. on Advanced Learning Technologies (ICALT 2016) (pp. 184–188). Austin, Texas: IEEE
Abstract: Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.
URI: http://hdl.handle.net/1820/7062
Appears in Collections:1. RAGE Publications

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