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

doi: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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-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 language	English
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	https://doi.org/10.1109/ICALT.2016.104
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)
Abbreviated title	ICALT 2016
Country/Territory	United States
City	Austin
Period	25/07/16 → 28/07/16
Internet address	https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7756054

Keywords

social media
textual complexity analysis
student performance
learning style

Access to Document

10.1109/ICALT.2016.104Licence: CC BY-NC-ND

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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)
European Union
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Project: Research

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 et al. / 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

@inproceedings{06b7fa0a18534c88b8412cd8e4a127d9,

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.",

keywords = "social media, textual complexity analysis, student performance, learning style",

author = "Elvira Popescu and Mihai Dascalu and Alexandru Becheru and Scott Crossly and Stefan Trausan-Matu",

year = "2016",

month = sep,

day = "27",

doi = "10.1109/ICALT.2016.104",

language = "English",

isbn = "978-1-4673-9041-5 ",

pages = "184--188",

editor = "Spector, {J. Michael} and Chin-Chung Tsai and Sampson, {Demetrios G} and Kinshuk and Ronghuai Huang and Nian-Shing Chen and Paul Resta",

booktitle = "The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016)",

publisher = "IEEE",

address = "United States",

note = "The 16th IEEE International Conference on<br/>Advanced Learning Technologies (ICALT 2016)<br/> : Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2016 ; Conference date: 25-07-2016 Through 28-07-2016",

url = "https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7756054",

}

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, Texas, 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 et al.
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 proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - Predicting Student Performance and Differences in Learning Styles based onTextual Complexity Indices applied on Blog and Microblog Posts

T2 - The 16th IEEE International Conference on<br/>Advanced Learning Technologies (ICALT 2016)<br/>

AU - Popescu, Elvira

AU - Dascalu, Mihai

AU - Becheru, Alexandru

AU - Crossly, Scott

AU - Trausan-Matu, Stefan

PY - 2016/9/27

Y1 - 2016/9/27

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.

KW - social media

KW - textual complexity analysis

KW - student performance

KW - learning style

U2 - 10.1109/ICALT.2016.104

DO - 10.1109/ICALT.2016.104

M3 - Conference Article in proceeding

SN - 978-1-4673-9041-5

SP - 184

EP - 188

BT - The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016)

A2 - Spector, J. Michael

A2 - Tsai, Chin-Chung

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A2 - Chen, Nian-Shing

A2 - Resta, Paul

PB - IEEE

Y2 - 25 July 2016 through 28 July 2016

ER -

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 CC, Sampson DG, Kinshuk, Huang R, Chen NS, 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 doi: 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

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Conference

Keywords

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