research-article

Eye tracking in computing education

Authors:

Teresa Busjahn,

Carsten Schulte,

Michael Hansen,

Roman Bednarik,

Petri Ihantola,

Galina Shchekotova,

Maria AntropovaAuthors Info & Claims

ICER '14: Proceedings of the tenth annual conference on International computing education research

Pages 3 - 10

https://doi.org/10.1145/2632320.2632344

Published: 28 July 2014 Publication History

Abstract

The methodology of eye tracking has been gradually making its way into various fields of science, assisted by the diminishing cost of the associated technology. In an international collaboration to open up the prospect of eye movement research for programming educators, we present a case study on program comprehension and preliminary analyses together with some useful tools.

The main contributions of this paper are (1) an introduction to eye tracking to study programmers; (2) an approach that can help elucidate how novices learn to read and understand programs and to identify improvements to teaching and tools; (3) a consideration of data analysis methods and challenges, along with tools to address them; and (4) some larger computing education questions that can be addressed (or revisited) in the context of eye tracking.

References

[1]

A. Allevato and S. H. Edwards. Discovering patterns in student activity on programming assignments. In ASEE Southeastern Section Annual Conference and Meeting, 2010.

[2]

V. M. G. Barrios, C. Gütl, A. M. Preis, K. Andrews, M. Pivec, F. Mödritscher, and C. Trummer. Adele: A framework for adaptive e-learning through eye tracking. In Proc. of IKnow, volume 4, pages 1--8. Citeseer, 2004.

[3]

R. Bednarik. Expertise-dependent visual attention strategies develop over time during debugging with multiple code representations. International J. of Human-Computer Studies, 70(2):143--155, 2012.

Digital Library

[4]

R. Bednarik, T. Busjahn, and C. Schulte. Eye movements in programming education: Analyzing the expert's gaze. Technical report, University of Eastern Finland, Joensuu, Finland, 2014.

[5]

R. Bednarik, N. Myller, E. Sutinen, and M. Tukiainen. Effects of experience on gaze behavior during program animation. In Proc. of 17th Annual Workshop of the Psychology of Programming Interest Group, pages 49--61, Sussex University, 2005.

[6]

R. Bednarik and M. Tukiainen. An eye-tracking methodology for characterizing program comprehension processes. In Proc. of the Symposium on Eye Tracking Research & Applications, pages 125--132. ACM, 2006.

Digital Library

[7]

T. Busjahn, C. Schulte, and A. Busjahn. Analysis of code reading to gain more insight in program comprehension. In Proc. of the 11th Koli Calling International Conference on Computing Education Research, pages 1--9, Koli, Finland, 2011. ACM.

Digital Library

[8]

L. Cowen, L. J. Ball, and J. Delin. An eye movement analysis of web page usability. In People and Computers XVI-Memorable Yet Invisible, pages 317--335. Springer, 2002.

[9]

M. E. Crosby and J. Stelovsky. How do we read algorithms? A case study. Computer, 23(1):24--35, 1990.

Digital Library

[10]

Q. Fan. The effects of beacons, comments, and tasks on program comprehension process in software maintenance. PhD thesis, University of Maryland at Baltimore County, Catonsville, MD, USA, 2010.

Digital Library

[11]

J. Helminen, P. Ihantola, and V. Karavirta. Recording and analyzing in-browser programming sessions. In Proc. of the 13th Koli Calling International Conference on Computing Education Research, pages 13--22, Koli, Finland, 2013. ACM.

Digital Library

[12]

V. A. Huemer, M. Hayashi, F. Renema, S. Elkins, J. W. McCandless, and R. S. McCann. Characterizing scan patterns in a spacecraft cockpit simulator: Expert vs. novice performance. Proc. of the Human Factors and Ergonomics Society Annual Meeting, 49(1):83--87, Sept. 2005.

[13]

A. Hyrskykari, S. Ovaska, P. Majaranta, K.-J. Räihä, and M. Lehtinen. Gaze path stimulation in retrospective think-aloud. J. of Eye Movement Research, 2(4):1--18, 2008.

[14]

M. Just and P. Carpenter. A theory of reading: From eye fixations to comprehension. Psychological Review, 87:329--354, 1980.

[15]

M.-J. Laakso and Simon, editors. Proceedings of the 13th Koli Calling International Conference on Computing Education Research, November 2013.

[16]

R. Lister, C. Fidge, and D. Teague. Further evidence of a relationship between explaining, tracing and writing skills in introductory programming. SIGCSE Bulletin, 41(3):161--165, 2009.

Digital Library

[17]

J. C. Nordbotten and M. E. Crosby. The effect of graphic style on data model interpretation. Information Systems J., 9(2):139--155, 1999.

[18]

C. Piech, M. Sahami, D. Koller, S. Cooper, and P. Blikstein. Modeling how students learn to program. In Proc. of the 43rd ACM Technical Symposium on Computer Science Education, SIGCSE '12, pages 153--160, NY, USA, 2012. ACM.

Digital Library

[19]

A. Poole and L. J. Ball. Eye tracking in human-computer interaction and usability research: Current status and future. In Prospects, Chapter in C. Ghaoui (Ed.): Encyclopedia of Human-Computer Interaction. Pennsylvania: Idea Group, Inc, 2005.

[20]

B. Sharif, M. Falcone, and J. Maletic. An eye-tracking study on the role of scan time in finding source code defects. In Proc. of the Symposium on Eye Tracking Research & Applications, pages 381--384, Santa Barbara, CA, 2012. ACM.

Digital Library

[21]

S. Sridharan, R. Bailey, A. McNamara, and C. Grimm. Subtle gaze manipulation for improved mammography training. In Proc. of the Symposium on Eye Tracking Research & Applications, pages 75--82, Santa Barbara, California, 2012. ACM.

Digital Library

[22]

R. Stein and S. E. Brennan. Another person's eye gaze as a cue in solving programming problems. In Proc. of the 6th international conference on Multimodal interfaces, pages 9--15, PA, USA, 2004. ACM.

Digital Library

[23]

R. Turner, M. Falcone, B. Sharif, and A. Lazar. An eye-tracking study assessing the comprehension of C++ and Python source code. In Proc. of the Symposium on Eye Tracking Research & Applications, pages 231--234, Safety Harbor, Florida, 2014. ACM.

Digital Library

[24]

H. Uwano, M. Nakamura, A. Monden, and K.-i. Matsumoto. Analyzing individual performance of source code review using reviewers' eye movement. In Proc. of the Symposium on Eye Tracking Research & Applications, pages 133--140, San Diego, California, 2006. ACM.

Digital Library

[25]

S. J. Vine, R. S. Masters, J. S. McGrath, E. Bright, and M. R. Wilson. Cheating experience: Guiding novices to adopt the gaze strategies of experts expedites the learning of technical laparoscopic skills. Surgery, 152(1):32--40, July 2012.

[26]

S. Yusuf, H. Kagdi, and J. I. Maletic. Assessing the comprehension of UML class diagrams via eye tracking. In Proc. of the 15th IEEE International Conference on Program Comprehension, pages 113--122, 2007.

Digital Library

Cited By

Hüsing SSparmann SSchulte CBolte M(2024)Identifying K-12 Students' Approaches to Using Worked Examples for Epistemic ProgrammingProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3655094(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3655094
Prather JReeves BLeinonen JMacNeil SRandrianasolo ABecker BKimmel BWright JBriggs B(2024)The Widening Gap: The Benefits and Harms of Generative AI for Novice ProgrammersProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671116(469-486)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671116
Liu PZhang CArditi DLi HDemirkesen S(2024)Using Eye-Tracking Technology to Assess the Effect of Daily Safety Training on Hazard Recognition SkillsIEEE Transactions on Engineering Management10.1109/TEM.2024.336723071(8548-8561)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3367230
Show More Cited By

Index Terms

Eye tracking in computing education
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

Recommendations

Beyond gaze: preliminary analysis of pupil dilation and blink rates in an fMRI study of program comprehension
EMIP '18: Proceedings of the Workshop on Eye Movements in Programming

Researchers have been employing psycho-physiological measures to better understand program comprehension, for example simultaneous fMRI and eye tracking to validate top-down comprehension models. In this paper, we argue that there is additional value in ...
Eye Tracking- Single Technology to Handle Multiple Domains
IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

Interdisciplinary research always yields an enhanced and different perspective on the problem. Solving a problem using many modalities and technologies gives us the complete picture of the problem and the best solution possible. My thesis is about ...
Eye&Head: Synergetic Eye and Head Movement for Gaze Pointing and Selection
UIST '19: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

Eye gaze involves the coordination of eye and head movement to acquire gaze targets, but existing approaches to gaze pointing are based on eye-tracking in abstraction from head motion. We propose to leverage the synergetic movement of eye and head, and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICER '14: Proceedings of the tenth annual conference on International computing education research

July 2014

186 pages

ISBN:9781450327558

DOI:10.1145/2632320

General Chairs:
Quintin Cutts
University of Glasgow, UK
,
Beth Simon
University of California, San Diego, USA
,
Brian Dorn
University of Nebraska, Omaha, USA

Copyright © 2014 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

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICER '14

Sponsor:

SIGCSE

ICER '14: International Computing Education Research Conference

August 11 - 13, 2014

Scotland, Glasgow, United Kingdom

Acceptance Rates

ICER '14 Paper Acceptance Rate 17 of 69 submissions, 25%;

Overall Acceptance Rate 189 of 803 submissions, 24%

Upcoming Conference

ICER 2025

Sponsor:
sigcse

ACM Conference on International Computing Education Research

August 3 - 6, 2025

Charlottesville , VA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
1,432
Total Downloads

Downloads (Last 12 months)133
Downloads (Last 6 weeks)9

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hüsing SSparmann SSchulte CBolte M(2024)Identifying K-12 Students' Approaches to Using Worked Examples for Epistemic ProgrammingProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3655094(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3655094
Prather JReeves BLeinonen JMacNeil SRandrianasolo ABecker BKimmel BWright JBriggs B(2024)The Widening Gap: The Benefits and Harms of Generative AI for Novice ProgrammersProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671116(469-486)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671116
Liu PZhang CArditi DLi HDemirkesen S(2024)Using Eye-Tracking Technology to Assess the Effect of Daily Safety Training on Hazard Recognition SkillsIEEE Transactions on Engineering Management10.1109/TEM.2024.336723071(8548-8561)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3367230
Molina AArroyo YLacave CRedondo MBravo COrtega M(2024)Eye tracking-based evaluation of accessible and usable interactive systems: tool set of guidelines and methodological issuesUniversal Access in the Information Society10.1007/s10209-023-01083-xOnline publication date: 11-Jan-2024
https://doi.org/10.1007/s10209-023-01083-x
Mansoor NPeterson CDodd MSharif B(2023)Assessing the Effect of Programming Language and Task Type on Eye Movements of Computer Science StudentsACM Transactions on Computing Education10.1145/363253024:1(1-38)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3632530
Shamy MFeitelson D(2023)Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and CodeACM Transactions on Applied Perception10.1145/357935720:2(1-20)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3579357
Park KWeill-Tessier PBrown NSharif BJensen NKölling M(2023)An eye tracking study assessing the impact of background styling in code editors on novice programmers’ code understandingProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600133(444-463)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600133
Lapierre HCharland PLéger P(2023)Looking “Under the hood” of learning computer programming: the emotional and cognitive differences between novices and beginnersComputer Science Education10.1080/08993408.2023.221403334:3(331-352)Online publication date: 25-May-2023
https://doi.org/10.1080/08993408.2023.2214033
Brumberger E(2022)Generational differences in viewing behaviors: an eye-tracking studyVisual Communication10.1177/1470357222111783922:1(128-151)Online publication date: 10-Nov-2022
https://doi.org/10.1177/14703572221117839
Paul MPodder PHassan M(2022)Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination TechniqueIEEE Access10.1109/ACCESS.2022.320604510(98612-98624)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3206045
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