research-article

On the Impact of Personality in Massive Open Online Learning

Authors:

Guanliang Chen,

Geert-Jan HoubenAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 121 - 130

https://doi.org/10.1145/2930238.2930240

Published: 13 July 2016 Publication History

Abstract

Massive Open Online Courses (MOOCs) have gained considerable momentum since their inception in 2011. They are, however, plagued by two issues that threaten their future: learner engagement and learner retention. MOOCs regularly attract tens of thousands of learners, though only a very small percentage complete them successfully. In the traditional classroom setting, it has been established that personality impacts different aspects of learning. It is an open question to what extent this finding translates to MOOCs: do learners' personalities impact their learning & learning behaviour in the MOOC setting? In this paper, we explore this question and analyse the personality profiles and learning traces of hundreds of learners that have taken a EX101x Data Analysis MOOC on the edX platform. We find learners' personality traits to only weakly correlate with learning as captured through the data traces learners leave on edX.

References

[1]

S. Adali and J. Golbeck. Predicting personality with social behavior. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining, ASONAM '12, pages 302--309, 2012.

Digital Library

[2]

Y. Bachrach, M. Kosinski, T. Graepel, P. Kohli, and D. Stillwell. Personality and patterns of facebook usage. In Proceedings of the 4th Annual ACM Web Science Conference, WebSci '12, pages 24--32, 2012.

Digital Library

[3]

G. Blickle. Personality traits, learning stratigies, and performance. European Journal of Personality, 10(5):337--352, 1996.

[4]

T. Chamorro-Premuzic and A. Furnham. Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality, 37(4):319--338, 2003.

[5]

S.-J. Chen and E. J. Caropreso. Influence of personality on online discussion. Journal of Interactive Online Learning, 3(2):1--17, 2004.

[6]

M. Clark and C. A. Schroth. Examining relationships between academic motivation and personality among college students. Learning and individual differences, 20(1):19--24, 2010.

[7]

D. A. Cobb-Clark and S. Schurer. The stability of big-five personality traits. Economics Letters, 115(1):11--15, 2012.

[8]

M. A. Conard. Aptitude is not enough: How personality and behavior predict academic performance. Journal of Research in Personality, 40(3):339--346, 2006.

[9]

T. Correa, A. W. Hinsley, and H. G. De Zuniga. Who interacts on the web?: The intersection of users' personality and social media use. Computers in Human Behavior, 26(2):247--253, 2010.

Digital Library

[10]

M. Cristani, A. Vinciarelli, C. Segalin, and A. Perina. Unveiling the multimedia unconscious: Implicit cognitive processes and multimedia content analysis. In Proceedings of the 21st ACM International Conference on Multimedia, MM '13, pages 213--222, 2013.

Digital Library

[11]

B. De Raad. The Big Five Personality Factors: The psycholexical approach to personality. Hogrefe & Huber Publishers, 2000.

[12]

M. Dennis, J. Masthoff, and C. Mellish. Adapting progress feedback and emotional support to learner personality. International Journal of Artificial Intelligence in Education, pages 1--55, 2015.

[13]

T. Farsides and R. Woodfield. Individual differences and undergraduate academic success: The roles of personality, intelligence, and application. Personality and Individual differences, 34(7):1225--1243, 2003.

[14]

B. Ferwerda, M. Schedl, and M. Tkalcic. Using Instagram Picture Features to Predict Users' Personality. In Q. Tian, N. Sebe, G.-J. Qi, B. Huet, R. Hong, and X. Liu, editors, MultiMedia Modeling, volume 9516 of Lecture Notes in Computer Science, pages 850--861. 2016.

Digital Library

[15]

R. Gao, B. Hao, S. Bai, L. Li, A. Li, and T. Zhu. Improving user profile with personality traits predicted from social media content. In Proceedings of the 7th ACM Conference on Recommender Systems, RecSys '13, pages 355--358, 2013.

Digital Library

[16]

J. Golbeck, C. Robles, M. Edmondson, and K. Turner. Predicting Personality from Twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, pages 149--156, 2011.

[17]

J. Golbeck, C. Robles, and K. Turner. Predicting personality with social media. In CHI '11 Extended Abstracts on Human Factors in Computing Systems, CHI EA '11, pages 253--262, 2011.

Digital Library

[18]

L. R. Goldberg. The development of markers for the big-five factor structure. Psychological assessment, 4(1):26--42, 1992.

[19]

P. J. Guo and K. Reinecke. Demographic differences in how students navigate through MOOCs. In Proceedings of the first ACM conference on Learning@Scale, pages 21--30, 2014.

Digital Library

[20]

D. Koller, A. Ng, C. Do, and Z. Chen. Retention and intention in massive open online courses. Educause Review, 48(3):62--63, 2013.

[21]

D. Koller, A. Ng, C. Do, and Z. Chen. Retention and intention in massive open online courses: In depth. Educause Review, 48(3):62--63, 2013.

[22]

M. Komarraju, S. J. Karau, R. R. Schmeck, and A. Avdic. The big five personality traits, learning styles, and academic achievement. Personality and Individual Differences, 51(4):472--477, 2011.

[23]

M. Kosinski, D. Stillwell, and T. Graepel. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15):5802--5805, 2013.

[24]

J. Lee and Y. Lee. Personality types and learners'interaction in web-based threaded discussion. Quarterly Review of Distance Education, 7(1):83--94, 2006.

[25]

A. Liaw and M. Wiener. Classification and regression by randomforest. R news, 2(3):18--22, 2002.

[26]

S. T. McAbee and F. L. Oswald. The criterion-related validity of personality measures for predicting gpa: A meta-analytic validity competition. Psychological Assessment, 25(2):532, 2013.

[27]

M. C. O'Connor and S. V. Paunonen. Big five personality predictors of post-secondary academic performance. Personality and Individual differences, 43(5):971--990, 2007.

[28]

A. E. Poropat. A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin, 135(2):322--338, 2009.

[29]

D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft. Our twitter profiles, our selves: Predicting personality with twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, pages 180--185, 2011.

[30]

C. E. Rasmussen. Gaussian processes in machine learning. In Advanced lectures on machine learning, pages 63--71. Springer, 2004.

[31]

M. Richardson, C. Abraham, and R. Bond. Psychological correlates of university students' academic performance: a systematic review and meta-analysis. Psychological bulletin, 138(2):353--387, 2012.

[32]

J. Robison, S. McQuiggan, and J. Lester. Intelligent Tutoring Systems: 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14--18, 2010, Proceedings, Part I, chapter Developing Empirically Based Student Personality Profiles for Affective Feedback Models, pages 285--295. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010.

Digital Library

[33]

O. Rodriguez. The concept of openness behind c and x-moocs (massive open online courses). Open Praxis, 5(1):67--73, 2013.

[34]

D. Rose. Personality as it relates to learning styles in online courses. In P. Resta, editor, Proceedings of Society for Information Technology & Teacher Education International Conference 2012, pages 827--831. Association for the Advancement of Computing in Education (AACE), 2012.

[35]

C. Spearman. The proof and measurement of association between two things. The American journal of psychology, 15(1):72--101, 1904.

[36]

C. Sumner, A. Byers, R. Boochever, and G. Park. Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on, volume 2, pages 386--393, 2012.

Digital Library

[37]

A. Vedel. The big five and tertiary academic performance: A systematic review and meta-analysis. Personality and Individual Differences, 71:66--76,\ 2014.

Cited By

Wu WZhou XShi XWu PLin XYang JHe L(2024)The Effect of Individual-Level Factors and Task Features on Interface Design for Rule-Verification Crowdsourcing TasksInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2332031(1-28)Online publication date: 16-Apr-2024
https://doi.org/10.1080/10447318.2024.2332031
Thongthip PIntawong KChernbumroong SWorragin PTongpaeng YPuritat K(2024)Design and analysis of personalized serious games for information literacy: catering to introverted and extraverted individuals through game elementsHumanities and Social Sciences Communications10.1057/s41599-024-03172-511:1Online publication date: 4-Jun-2024
https://doi.org/10.1057/s41599-024-03172-5
Aida S(2023)Impact of E-Learning Orientation, Moodle Usage, and Learning Planning on Learning Outcomes in On-Demand LecturesEducation Sciences10.3390/educsci1310100513:10(1005)Online publication date: 3-Oct-2023
https://doi.org/10.3390/educsci13101005
Show More Cited By

Index Terms

On the Impact of Personality in Massive Open Online Learning

Recommendations

Supporting learners' self-regulated learning in Massive Open Online Courses
Abstract
In MOOCs, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated learning (SRL) in order to successfully study in a MOOC. Learners however often struggle to self-...
Highlights
- Learners struggle to regulate their learning in massive open online courses (MOOCs).
- A self-regulated learning (SRL) intervention was implemented in three MOOCs.
- Learners' SRL was measured with trace data variables.
- ...
Benefit and Cost Analysis of Massive Open Online Courses: Pedagogical Implications on Higher Education

There has been much research done on online learning including research on online educational activities and methods. The use of technology is gaining rising importance in higher education due to the benefits that it brings. In terms of adopting new ...
Benefit and Cost Analysis of Massive Open Online Courses: Pedagogical Implications on Higher Education

There has been much research done on online learning including research on online educational activities and methods. The use of technology is gaining rising importance in higher education due to the benefits that it brings. In terms of adopting new ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

The Extension School of the Delft University of Technology
The Leiden-Delft-Erasmus Centre for Education and Learning

Conference

UMAP '16

Sponsor:

UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

Acceptance Rates

UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
437
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wu WZhou XShi XWu PLin XYang JHe L(2024)The Effect of Individual-Level Factors and Task Features on Interface Design for Rule-Verification Crowdsourcing TasksInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2332031(1-28)Online publication date: 16-Apr-2024
https://doi.org/10.1080/10447318.2024.2332031
Thongthip PIntawong KChernbumroong SWorragin PTongpaeng YPuritat K(2024)Design and analysis of personalized serious games for information literacy: catering to introverted and extraverted individuals through game elementsHumanities and Social Sciences Communications10.1057/s41599-024-03172-511:1Online publication date: 4-Jun-2024
https://doi.org/10.1057/s41599-024-03172-5
Aida S(2023)Impact of E-Learning Orientation, Moodle Usage, and Learning Planning on Learning Outcomes in On-Demand LecturesEducation Sciences10.3390/educsci1310100513:10(1005)Online publication date: 3-Oct-2023
https://doi.org/10.3390/educsci13101005
Akhuseyinoglu KBrusilovsky P(2022)Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual DifferencesFrontiers in Artificial Intelligence10.3389/frai.2022.8073205Online publication date: 15-Feb-2022
https://doi.org/10.3389/frai.2022.807320
Akhuseyinoglu KBrusilovsky P(2021)Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online LearningProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456834(201-212)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456834
García-Molina SAlario-Hoyos CMoreno-Marcos PMuñoz-Merino PEstévez-Ayres IDelgado Kloos C(2020)An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion ForumApplied Sciences10.3390/app1101009511:1(95)Online publication date: 24-Dec-2020
https://doi.org/10.3390/app11010095
Moreno-Marcos PMuñoz-Merino PAlario-Hoyos CDelgado Kloos C(2020)Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online LearningApplied Sciences10.3390/app1005172210:5(1722)Online publication date: 3-Mar-2020
https://doi.org/10.3390/app10051722
Al Abdullatif HVelazquez-Iturbide J(2020)Who Will Continue Using MOOCs in the Future? Personality Traits PerspectiveIEEE Access10.1109/ACCESS.2020.29791808(52841-52851)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2979180
Moreno-Marcos PPong TMunoz-Merino PDelgado Kloos C(2020)Analysis of the Factors Influencing Learners’ Performance Prediction With Learning AnalyticsIEEE Access10.1109/ACCESS.2019.29635038(5264-5282)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2019.2963503
Alabdullatif HVelázquez-Iturbide J(2020)Personality Traits and Intention to Continue Using Massive Open Online Courses (ICM) in Spain: The Mediating Role of MotivationsInternational Journal of Human–Computer Interaction10.1080/10447318.2020.1805873(1-15)Online publication date: 2-Sep-2020
https://doi.org/10.1080/10447318.2020.1805873
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten