research-article

An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations

Authors:

Matthew JohnsonAuthors Info & Claims

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Pages 125 - 134

https://doi.org/10.1145/2460296.2460321

Published: 08 April 2013 Publication History

Abstract

This paper presents an eye-tracking study of notational, informational, and emotional aspects of nine different notational systems (Skill Meters, Smilies, Traffic Lights, Topic Boxes, Collective Histograms, Word Clouds, Textual Descriptors, Table, and Matrix) and three different information states (Weak, Average, & Strong) used to represent student's learning. Findings from the eye-tracking study show that higher emotional activation was observed for the metaphorical notations of traffic lights and smilies and collective representations. Mean view time was higher for representations of the "average" informational learning state. Qualitative data analysis of the think-aloud comments and post-study interview show that student participants reflected on the meaning-making opportunities and action-taking possibilities afforded by the representations. Implications for the design and evaluation of learning analytics representations and discourse environments are discussed.

References

[1]

Blackwell, A. and Green, T. Notational systems--the cognitive dimensions of notations framework. HCI Models, Theories, and Frameworks: Toward an Interdisciplinary Science. Morgan Kaufmann (2003).

[2]

Bradley, M. M., Miccoli, L., Escrig, M. A. and Lang, P. J. The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology, 45, 4 (2008), 602--607.

[3]

Bull, S., Gakhal, I., Grundy, D., Johnson, M., Mabbott, A. and Xu, J. Preferences in Multiple View Open Learner Models. In M. Wolpers, P. A. Kirschner, M. Scheffel, S. Lindstaedt and V. Dimitrova (eds.) EC-TEL 2010, (2010), 476--481.

Digital Library

[4]

Bull, S. and Kay, J. Student Models that Invite the Learner In: The SMILI Open Learner Modelling Framework. International Journal of Artificial Intelligence in Education 17, 2 (2007), 89--120.

Digital Library

[5]

Cohen, A. The Gamification of Education. Futurist, 45, 5 (2011), 16--17.

[6]

Cordova, D. and Lepper, M. Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88, 4 (1996), 715--730.

[7]

Eales, R. T. J., Hall, T. and Bannon, L. J. The motivation is the message: comparing CSCL in different settings. In Proc. Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community, International Society of the Learning Sciences (2002), 310--317.

Digital Library

[8]

Edmonds, S. Gamification of learning. Training and Development in Australia, 38, 6 (2011), 20--22.

[9]

Gibson, J. J. The ecological approach to visual perception. Houghton Mifflin, Boston, 1979.

[10]

Green, T. Cognitive dimensions of notations. People and Computers V (1989), 443--460.

Digital Library

[11]

Green, T. and Blackwell, A. Cognitive Dimensions of Information Artefacts: a tutorial. BCS HCI Conference (1998).

[12]

Lee, J. J. and Hammer, J. Gamification in Education: What, How, Why Bother? Academic Exchange Quarterly, 15, 2 (2011), 146.

[13]

Partala, T. and Surakka, V. Pupil size variation as an indication of affective processing. International Journal of Human-Computer Studies, 59, 1 (2003), 185--198.

Digital Library

[14]

Renaud, C. and Wagoner, B. The Gamification of Learning. Principal Leadership, 12, 1 (2011), 56--59.

[15]

Smith-Robbins, S. This game sucks": How to improve the gamification of education. EDUCAUSE Review, 46, 1 (2011), 58--59.

[16]

Suthers, D. Representational Bias as Guidance for Learning Interactions: A Research Agenda. In Proc. 9th World Conference on Artificial Intelligence in Education (AIED'97), July 19--23, 1999 (1999).

[17]

Suthers, D. The Representational Guidance Project. Laboratory for Interactive Learning Technologies (LILT). City, n.d.

[18]

Suthers, D. Towards a Systematic Study of Representational Guidance for Collaborative Learning Discourse. Journal of Universal Computer Science, 7, 3 (2001), http://lilt.ics.hawaii.edu/lilt/papers/2001/Suthers-JUCS-2001.pdf.

[19]

Suthers, D. and Hundhausen, C. An Experimental Study of the Effects of Representational Guidance on Collaborative Learning. Journal of the Learning Sciences, 12, 2 (2003), 183--219.

[20]

Suthers, D., Vatrapu, R., Medina, R., Joseph, S. and Dwyer, N. Beyond Threaded Discussion: Representational Guidance in Asynchronous Collaborative Learning Environments. Computers and Education, 50, 4 (2008), 1103--1127.

Digital Library

[21]

Vatrapu, R. Explaining Culture: An Outline of a Theory of Socio-Technical Interactions. Proceedings of the 3rd International Conference on Intercultural Collaboration (ICIC 2010) (2010), 111--120.

Digital Library

[22]

Vatrapu, R. Toward a Theory of Socio-Technical Interactions in Technology Enhanced Learning Environments. In U. Cress, V. Dimitrova and M. Specht (eds.) EC-TEL 2009, Lecture Notes in Computer Science (LNCS) 5794, (2009), 694--699.

Digital Library

[23]

Vatrapu, R., Reimann, P. and Hussain, A. Towards Teaching Analytics: Repertory Grids for Formative Assessment. In Proc. International Conference of the Learning Sciences (ICLS) 2012 (2012).

[24]

Vatrapu, R., Tanveer, U. and Hussain, A. Towards teaching analytics: communication and negotiation tool (CoNeTo). In Proc. Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design, ACM (2012), 775--776.

Digital Library

[25]

Vatrapu, R., Teplovs, C., Fujita, N. and Bull, S. Towards Visual Analytics for Teachers' Dynamic Diagnostic Pedagogical Decision-Making. Paper presented at the 1st International Conference on Learning Analytics & Knowledge (LAK 2011), Banff, Canada. (2011).

Digital Library

[26]

Winn, W. Cognitive Perspectives in Psychology. In D. H. Jonassen (ed.) Handbook of research on educational communications and technology, (2004), 79--112.

Cited By

Ahrar ADoroodian MHatala M(2025)Exploring Eye-tracking Features to Understand Students' Sensemaking of Learning Analytics DashboardsProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706543(931-937)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706543
Shabnam Ara STanuja RManjula SVenugopal K(2023)A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning PortalsJournal of Educational Technology Systems10.1177/0047239523118584352:2(245-273)Online publication date: 22-Aug-2023
https://doi.org/10.1177/00472395231185843
Jones HFitzgerald R(2023)The Role of Analytics When Supporting Staff and Students in the Virtual Learning EnvironmentTechnology-Enhanced Learning and the Virtual University10.1007/978-981-99-4170-4_11(187-200)Online publication date: 21-Sep-2023
https://doi.org/10.1007/978-981-99-4170-4_11
Show More Cited By

Index Terms

An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Collaborative and social computing
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Second International Workshop on Teaching Analytics
LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Teaching Analytics is conceived as a subfield of learning analytics that focuses on the design, development, evaluation, and education of visual analytics methods and tools for teachers in primary, secondary, and tertiary educational settings. The ...
Towards visual analytics for teachers' dynamic diagnostic pedagogical decision-making
LAK '11: Proceedings of the 1st International Conference on Learning Analytics and Knowledge

The focus of this paper is to delineate and discuss design considerations for supporting teachers' dynamic diagnostic decision-making in classrooms of the 21st century. Based on the Next Generation Teaching Education and Learning for Life (NEXT-TELL) ...
The design and implementation of a teaching and learning analytics tool in a face-to-face, small-sized course in China’s higher education
Abstract
The data-intensive research paradigm calls for using educational and learning data to generate actionable insights and improve the instruction and learning quality. Although previous research designed and employed teaching analytics or learning ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

April 2013

300 pages

ISBN:9781450317856

DOI:10.1145/2460296

Editors:
Dan Suthers
University of Hawaii, USA
,
Katrien Verbert
Eindhoven University of Technology, The Netherlands
,
Erik Duval
University of Leuven, Belgium
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, Ecuador

Copyright © 2013 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

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
K.U. Leuven: K.U. Leuven
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NEXT-TELL - Next Generation Teaching, Education and Learning for Life
Seventh Framework Programme

Conference

LAK '13

Sponsor:

SIGWEB
K.U. Leuven
SIGCHI

LAK '13: Third Conference on Learning Analytics and Knowledge

April 8 - 13, 2013

Leuven, Belgium

Acceptance Rates

LAK '13 Paper Acceptance Rate 16 of 58 submissions, 28%;

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
603
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)2

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ahrar ADoroodian MHatala M(2025)Exploring Eye-tracking Features to Understand Students' Sensemaking of Learning Analytics DashboardsProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706543(931-937)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706543
Shabnam Ara STanuja RManjula SVenugopal K(2023)A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning PortalsJournal of Educational Technology Systems10.1177/0047239523118584352:2(245-273)Online publication date: 22-Aug-2023
https://doi.org/10.1177/00472395231185843
Jones HFitzgerald R(2023)The Role of Analytics When Supporting Staff and Students in the Virtual Learning EnvironmentTechnology-Enhanced Learning and the Virtual University10.1007/978-981-99-4170-4_11(187-200)Online publication date: 21-Sep-2023
https://doi.org/10.1007/978-981-99-4170-4_11
Jones HFitzgerald R(2023)The Role of Analytics When Supporting Staff and Students in the Virtual Learning EnvironmentTechnology-Enhanced Learning and the Virtual University10.1007/978-981-19-9438-8_11-1(1-14)Online publication date: 30-Apr-2023
https://doi.org/10.1007/978-981-19-9438-8_11-1
Hoyos AVelasquez J(2020)Teaching Analytics: Current Challenges and Future DevelopmentIEEE Revista Iberoamericana de Tecnologias del Aprendizaje10.1109/RITA.2020.297924515:1(1-9)Online publication date: Feb-2020
https://doi.org/10.1109/RITA.2020.2979245
Türkay SSeaton DAng AMandryk RHancock MPerry MCox A(2018)IteroExtended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3170427.3188474(1-6)Online publication date: 20-Apr-2018
https://dl.acm.org/doi/10.1145/3170427.3188474
Brusilovsky PSharples M(2017)EditorialIEEE Transactions on Learning Technologies10.1109/TLT.2017.267100010:1(1-2)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TLT.2017.2671000
Mejia CFlorian BVatrapu RBull SGomez SFabregat R(2017)A Novel Web-Based Approach for Visualization and Inspection of Reading Difficulties on University StudentsIEEE Transactions on Learning Technologies10.1109/TLT.2016.262629210:1(53-67)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TLT.2016.2626292
Vigentini LClayphan AZhang XChitsaz M(2017)Overcoming the MOOC Data Deluge with Learning Analytic DashboardsLearning Analytics: Fundaments, Applications, and Trends10.1007/978-3-319-52977-6_6(171-198)Online publication date: 18-Feb-2017
https://doi.org/10.1007/978-3-319-52977-6_6
Cope BKalantzis M(2016)Big Data Comes to SchoolAERA Open10.1177/23328584166419072:2Online publication date: 1-Apr-2016
https://doi.org/10.1177/2332858416641907
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