Projects per year
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.
Original language | English |
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Title of host publication | The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) |
Subtitle of host publication | Advanced Technologies for Supporting Open Access to Formal and Informal Learning |
Editors | J. Michael Spector, Chin-Chung Tsai, Demetrios G Sampson, Kinshuk, Ronghuai Huang, Nian-Shing Chen, Paul Resta |
Publisher | IEEE |
Pages | 184-188 |
ISBN (Print) | 978-1-4673-9041-5 |
DOIs | |
Publication status | Published - 27 Sept 2016 |
Externally published | Yes |
Event | The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) : Advanced Technologies for Supporting Open Access to Formal and Informal Learning - Austin, United States Duration: 25 Jul 2016 → 28 Jul 2016 https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7756054 |
Conference
Conference | The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) |
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Abbreviated title | ICALT 2016 |
Country/Territory | United States |
City | Austin |
Period | 25/07/16 → 28/07/16 |
Internet address |
Keywords
- social media
- textual complexity analysis
- student performance
- learning style
Fingerprint
Dive into the research topics of 'Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study'. Together they form a unique fingerprint.Projects
- 1 Finished
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Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
1/02/15 → 31/07/19
Project: Research