short-paper

Differences of online learning behaviors and eye-movement between students having different personality traits

Authors:

Yongkang XiaoAuthors Info & Claims

MIE 2017: Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education

Pages 71 - 75

https://doi.org/10.1145/3139513.3139527

Published: 13 November 2017 Publication History

Abstract

The information technologies are integrated into education so that mass data is available reflecting each action of students in online environments. Numerous studies have exploited these data to do the learning analytics.In this paper, we aim at achieving the show of personalized indicators for students per personality trait on the learning analytics dashboard (LAD) and present the preliminary results. First, we employ learning behavior engagement (LBE) to describe students' learning behaviors, exploited to analyze the significant differences among students having different personality traits. In experiments, fifteen behavioral indicators are tested. The experimental results show that there are significant differences about some behavioral indicators among personality traits. Second, some of these behavioral indicators are presented on the LAD and distributed in each area of interest (AOI). Hence, students can visualize their behavioral data that they care about in AOIs anytime in the learning process. Through the analysis of eye-movement including the fixation duration, fixation count, heat map and track map, we have found that there are significant differences about some visual indicators in AOIs. This is partly consistent with the results of behavioral indicators.

References

[1]

M.R. Barrick and M.K. Mount. 1991. The Big Five Personality Dimensions and Job Performance: A Meta-analysis. Personnel Psychology 51, 1 (1991), 849–857.

[2]

T. Chamorro-Premuzic and A. Furnham. 2008. Personality Intelligence and Approaches to Learning as Predictors of Academic Performance. Pers. Individ. Dif 44, 7 (2008), 1596–1603.

[3]

P.T. Costa and R.R. McCrae. 1992. Revised Neo Personality Inventory (neo pi-r) and Neo Five-factor Inventory (NEOFFI). Psychological Assessment Resources (1992), 17–20.

[4]

S.E. Hampson and L.R. Goldberg. 2006. A First Large Cohort Study of Personality Trait Stability over the 40 Years Between Elementary School and Midlife. Journal of Personality and Social Psychology 91, 4 (2006), 763–779.

[5]

M.A. Just and P.A Carpenter. 1980. A Theory of Reading: From Eye Fixations to Comprehension. Psychological Review 87 (1980), 329–354.

[6]

S.J. Karau M. Komarraju and R.R. Schmeck. 2009. Role of the Big Five Personality Traits in Predicting College Students’ Academic Motivation and Achievement. Learn. Individ. Differ 19, 1 (2009), 47–52.

[7]

S.B. Miles and D. Stipek. 2006. Contemporaneous and Longitudinal Associations Between Social Behavior and Literacy Achievement in a Sample of Low-Income Elementary School Children. Child Development 1 (Feb. 2006), 103–117.

[8]

E. Molleman. 2005. Diversity in Demographic Characteristics, Abilities and Personality Traits: Do Faultlines Affect Team Functioning? Gr. Decis. Negot 14, 3 (2005), 173–193.

[9]

P. Dolog N. Henze and W. Nejdl. 2004. Reasoning and Ontologies for Personalized E-learning in the Semantic Web. Educational Technology and Society 7, 4 (2004), 82–97.

[10]

M. Samancioglu N. Ibrahimoglu, I. Unaldi and M. Baglibel. 2013. The Relationship between Personality Traits and Learning Styles: a Cluster Analysis. Asian Journal of Management Sciences and Education 2, 3 (2013), 93–108.

[11]

J. Tramper O. Sessink, R. Beeftink and R. Hartog. 2003. Author-Defined Storage in the Next Generation Learning Management Systems. In 3rd IEEE international conference on advanced learning technologies (ICALT’03). 57–61.

[12]

A.A. Pina. 2012. An Overview of Learning Management Systems. Virtual Learning Environments: Concepts, Methodologies, Tools and Applications. USA: IGI Global Differences of Online Learning Behaviors and Eye-Movement... MIE’17, November 13, 2017, Glasgow, UK (2012), 33–51.

[13]

T. Ryan and S. Xenos. 2011. Who Uses Facebook? An Investigation into the Relationship between the Big Five, Shyness, Narcissism, Loneliness and Facebook Usage. Computers in Human Behavior 27, 5 (2011), 1658–1664.

Digital Library

[14]

G. Siemens and R.S. Baker. 2012. Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. In 2nd International Conference Learning Analytics Knowledge. 252–254.

Digital Library

[15]

H.M. Truong. 2016. Integrating Learning Styles and Adaptive E-learning System: Current Developments, Problems and Opportunities. Computers in Human Behavior 55 (2016), 1185–1193.

Digital Library

[16]

J.J. Elshout V.V. Busato, F.J. Prins and C. Hamaker. 1998. The Relation between Learning Styles, the Big Five Personality Traits and Achievement Motivation in Higher Education. Personality and Individual Differences 26, 1 (1998), 129–140.

[17]

T. Waits and L. Lewis. 2003. Distance Education at Degree-granting Postsecondary institution 2001-2002. Technical Report. National Center for Education Statistics, Washington, DC. Abstract 1 Introduction 2 Difference Of Indicators Among Personality Traits 2.1 Identifying Personality Traits 2.2 Difference of Behavior Indicators 3 Difference Of Indicators In AOIs 3.1 Description of AOIs 3.2 Difference of Visual Indicators 4 Conclusions Acknowledgments References

Cited By

Balaskas SKoutroumani MRigou MSirmakessis S(2024)From Eye Movements to Personality Traits: A Machine Learning Approach in Blood Donation AdvertisingAI10.3390/ai50200345:2(635-666)Online publication date: 10-May-2024
https://doi.org/10.3390/ai5020034
Mu SCui MHuang X(2020)Multimodal Data Fusion in Learning Analytics: A Systematic ReviewSensors10.3390/s2023685620:23(6856)Online publication date: 30-Nov-2020
https://doi.org/10.3390/s20236856
Lai SSun BWu FXiao R(2020)Automatic Personality Identification Using Students’ Online Learning BehaviorIEEE Transactions on Learning Technologies10.1109/TLT.2019.292422313:1(26-37)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TLT.2019.2924223

Index Terms

Differences of online learning behaviors and eye-movement between students having different personality traits
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

MIE 2017: Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education

November 2017

75 pages

ISBN:9781450355575

DOI:10.1145/3139513

Copyright © 2017 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]

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Published: 13 November 2017

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ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 13, 2017

Glasgow, UK

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Cited By

Balaskas SKoutroumani MRigou MSirmakessis S(2024)From Eye Movements to Personality Traits: A Machine Learning Approach in Blood Donation AdvertisingAI10.3390/ai50200345:2(635-666)Online publication date: 10-May-2024
https://doi.org/10.3390/ai5020034
Mu SCui MHuang X(2020)Multimodal Data Fusion in Learning Analytics: A Systematic ReviewSensors10.3390/s2023685620:23(6856)Online publication date: 30-Nov-2020
https://doi.org/10.3390/s20236856
Lai SSun BWu FXiao R(2020)Automatic Personality Identification Using Students’ Online Learning BehaviorIEEE Transactions on Learning Technologies10.1109/TLT.2019.292422313:1(26-37)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TLT.2019.2924223

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