Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study

Elvira Popescu, Mihai Dascalu, Alexandru Becheru, Scott Crossly, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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 languageEnglish
Title of host publicationThe 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016)
Subtitle of host publicationAdvanced Technologies for Supporting Open Access to Formal and Informal Learning
EditorsJ. Michael Spector, Chin-Chung Tsai, Demetrios G Sampson, Kinshuk, Ronghuai Huang, Nian-Shing Chen, Paul Resta
PublisherIEEE
Pages184-188
ISBN (Print)978-1-4673-9041-5
DOIs
Publication statusPublished - 27 Sep 2016
Externally publishedYes
EventThe 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 201628 Jul 2016
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7756054

Conference

ConferenceThe 16th IEEE International Conference on
Advanced Learning Technologies (ICALT 2016)
Abbreviated titleICALT 2016
CountryUnited States
CityAustin
Period25/07/1628/07/16
Internet address

Fingerprint

weblog
social media
learning
performance
student
content analysis
learning environment

Keywords

  • social media
  • textual complexity analysis
  • student performance
  • learning style

Cite this

Popescu, E., Dascalu, M., Becheru, A., Crossly, S., & Trausan-Matu, S. (2016). Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study . In J. M. Spector, C-C. Tsai, D. G. Sampson, Kinshuk, R. Huang, N-S. Chen, & P. Resta (Eds.), The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016): Advanced Technologies for Supporting Open Access to Formal and Informal Learning (pp. 184-188). IEEE. https://doi.org/10.1109/ICALT.2016.104
Popescu, Elvira ; Dascalu, Mihai ; Becheru, Alexandru ; Crossly, Scott ; Trausan-Matu, Stefan. / Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts : A Preliminary Study . The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016): Advanced Technologies for Supporting Open Access to Formal and Informal Learning. editor / J. Michael Spector ; Chin-Chung Tsai ; Demetrios G Sampson ; Kinshuk ; Ronghuai Huang ; Nian-Shing Chen ; Paul Resta. IEEE, 2016. pp. 184-188
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title = "Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study",
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.",
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Popescu, E, Dascalu, M, Becheru, A, Crossly, S & Trausan-Matu, S 2016, Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study . in JM Spector, C-C Tsai, DG Sampson, Kinshuk, R Huang, N-S Chen & P Resta (eds), The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016): Advanced Technologies for Supporting Open Access to Formal and Informal Learning. IEEE, pp. 184-188, The 16th IEEE International Conference on
Advanced Learning Technologies (ICALT 2016)
, Austin, United States, 25/07/16. https://doi.org/10.1109/ICALT.2016.104

Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts : A Preliminary Study . / Popescu, Elvira; Dascalu, Mihai; Becheru, Alexandru; Crossly, Scott; Trausan-Matu, Stefan.

The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016): Advanced Technologies for Supporting Open Access to Formal and Informal Learning. ed. / J. Michael Spector; Chin-Chung Tsai; Demetrios G Sampson; Kinshuk; Ronghuai Huang; Nian-Shing Chen; Paul Resta. IEEE, 2016. p. 184-188.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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N2 - 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.

AB - 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.

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Popescu E, Dascalu M, Becheru A, Crossly S, Trausan-Matu S. Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts: A Preliminary Study . In Spector JM, Tsai C-C, Sampson DG, Kinshuk, Huang R, Chen N-S, Resta P, editors, The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016): Advanced Technologies for Supporting Open Access to Formal and Informal Learning. IEEE. 2016. p. 184-188 https://doi.org/10.1109/ICALT.2016.104