skip to main content
10.1145/3545945.3569749acmconferencesArticle/Chapter ViewAbstractPublication PagessigcseConference Proceedingsconference-collections
research-article
Public Access

Mind the Gap: The Illusion of Skill Acquisition in Computational Thinking

Published: 03 March 2023 Publication History

Abstract

With the advent of online educational platforms and the advances in pedagogical technologies, self-directed learning has emerged as one of the most popular modes of learning. Distance education---elevated by the COVID-19 pandemic---involves methods of instruction through a variety of remote activities which often rely on educational videos for mastery. In the absence of direct student engagement, the asynchronous nature of remote activities may deteriorate the quality of education for learners. Students often have an illusion of skill acquisition after watching videos, which results in overestimation of abilities and skills. We focus on the efficacy of skill acquisition through interactive technologies and assess their impact on computational thinking in comparison with delivery through other traditional media (e.g. videos and texts). In particular, we investigate the relationship between actual learning, perception of learning, and learners' confidence in adult learners. Our results reveal intriguing observations about the role of interactivity and visualization and their implications on the pedagogical design for self-directed learning modules.

References

[1]
Anders Berglund, Anna Eckerdal, Arnold Pears, Philip East, Päivi Kinnunen, Lauri Malmi, Robert McCartney, Jan-Erik Moström, Laurie Murphy, Mark Ratcliffe, et al. 2009. Learning computer science: Perceptions, actions and roles. European Journal of Engineering Education 34, 4 (2009), 327--338.
[2]
Benjamin Samuel Bloom, Committee of College, and University Examiners. 1964. Taxonomy of educational objectives. Vol. 2. Longmans, Green New York.
[3]
John Brooke. 2013. SUS: A Retrospective. Journal of Usability Studies 8, 2 (2013), 29--40.
[4]
Shana K. Carpenter, Miko M. Wilford, Nate Kornell, and Kellie M. Mullaney. 2013. Appearances can be deceiving: instructor fluency increases perceptions of learning without increasing actual learning. Psychonomic Bulletin & Review 20, 6 (2013), 1350--1356. https://doi.org/10.3758/s13423-013-0442-z
[5]
Edward Deci and Richard M Ryan. 1985. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.
[6]
Louis Deslauriers, Logan S McCarty, Kelly Miller, Kristina Callaghan, and Greg Kestin. 2019. Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences 116, 39 (2019), 19251--19257.
[7]
Mohammed F. Farghally, Kyu Han Koh, Hossameldin Shahin, and Clifford A. Shaffer. 2017. Evaluating the Effectiveness of Algorithm Analysis Visualizations. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE '17). Association for Computing Ma- chinery, New York, NY, USA, 201--206. https://doi.org/10.1145/3017680.3017698
[8]
James Ferris and Hadi Hosseini. 2020. MatchU: An Interactive Matching Platform. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13606--13607.
[9]
D. Gale and L. S. Shapley. 1962. College Admissions and the Stability of Marriage. The American Mathematical Monthly 69, 1 (1962), 9--15. https://doi.org/10.1080/ 00029890.1962.11989827 arXiv:https://doi.org/10.1080/00029890.1962.11989827
[10]
Maxwell Hartt, Hadi Hosseini, and Mehrnaz Mostafapour. 2020. Game On: Exploring the Effectiveness of Game-based Learning. Planning Practice & Research 35, 5 (2020), 589--604.
[11]
Hadi Hosseini, Maxwell Hartt, and Mehrnaz Mostafapour. 2019. Learning IS Child's Play: Game-Based Learning in Computer Science Education. Journal of Planning Education and Research, ACM Transactions on Computing Education (TOCE) forthcoming (2019).
[12]
Hadi Hosseini and Laurel Perweiler. 2019. Are You Game?. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE '19). Association for Computing Machinery, New York, NY, USA, 866--872. https://doi.org/10.1145/3287324.3287411
[13]
Essi Isohanni and Hannu-Matti Järvinen. 2014. Are Visualization Tools Used in Programming Education? By Whom, How, Why, and Why Not?. In Proceedings of the 14th Koli Calling International Conference on Computing Education Research. Association for Computing Machinery, New York, NY, USA, 35--40.
[14]
Michael Kardas and Ed O'Brien. 2018. Easier seen than done: Merely watching others perform can foster an illusion of skill acquisition. Psychological science (2018).
[15]
Michael Kardas and Ed O'Brien. 2018. Easier Seen Than Done: Merely Watching Others Perform Can Foster an Illusion of Skill Acquisition. Psychol Sci 29, 4 (Apr 2018), 521--536. https://doi.org/10.1177/0956797617740646
[16]
Zoltan Katai. 2014. Selective Hiding for Improved Algorithmic Visualization. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (Uppsala, Sweden) (ITiCSE '14). Association for Computing Machinery, New York, NY, USA, 33--38. https://doi.org/10.1145/2591708.2591734
[17]
Kerri-Ann Kuhn and Sharyn Rundle-Thiele. 2009. Curriculum alignment: Student perception of learning achievement measures. International Journal of Teaching and Learning in Higher Education 21, 3 (2009), 351--361.
[18]
Anders Larrabee Sønderlund, Emily Hughes, and Joanne Smith. 2019. The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology 50, 5 (2019), 2594--2618.
[19]
Meng-Yun Liao, Ching-Ying Sung, Hao-Chuan Wang, Wen-Chieh Lin, and Fu-Yin Cherng. 2019. Embodying Historical Learners' Messages as Learning Companions in a VR Classroom. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA '19). Association for Computing Machinery, New York, NY, USA, 1--6. https://doi.org/10.1145/ 3290607.3312861
[20]
Junhua Liu, Lionell Loh, Ernest Ng, Yijia Chen, Kristin L. Wood, and Kwan Hui Lim. 2020. Self-Evolving Adaptive Learning for Personalized Education. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (Virtual Event, USA) (CSCW '20 Compan- ion). Association for Computing Machinery, New York, NY, USA, 317--321. https://doi.org/10.1145/3406865.3418326
[21]
Marc Najork. 2001. Web-Based Algorithm Animation. In Proceedings of the 38th Annual Design Automation Conference (Las Vegas, Nevada, USA) (DAC '01). Association for Computing Machinery, New York, NY, USA, 506--511.
[22]
Thomas L. Naps, James R. Eagan, and Laura L. Norton. 2000. JHAVÉ-an Environment to Actively Engage Students in Web-Based Algorithm Visualizations. In Proceedings of the Thirty-First SIGCSE Technical Symposium on Computer Sci- ence Education (Austin, Texas, USA) (SIGCSE '00). Association for Computing Machinery, New York, NY, USA, 109--113. https://doi.org/10.1145/330908.331829
[23]
Thomas L. Naps, Guido Rößling, Vicki Almstrum, Wanda Dann, Rudolf Fleischer, Chris Hundhausen, Ari Korhonen, Lauri Malmi, Myles McNally, Susan Rodger, and J. Ángel Velázquez-Iturbide. 2002. Exploring the Role of Visualization and Engagement in Computer Science Education. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (Aarhus, Denmark) (ITiCSE-WGR '02). Association for Computing Machinery, New York, NY, USA, 131--152. https://doi.org/10.1145/960568.782998
[24]
Jakob Nielsen. 1994. Usability inspection methods. In Conference companion on Human factors in computing systems. 413--414.
[25]
Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI conference on Human factors in computing systems. 249--256.
[26]
Ed O'Brien. 2019. Enjoy it again: Repeat experiences are less repetitive than people think. Journal of Personality and Social Psychology 116, 4 (2019), 519.
[27]
Jungkook Park, Yeong Hoon Park, Suin Kim, and Alice Oh. 2017. Eliph: Effective Visualization of Code History for Peer Assessment in Programming Education. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). Association for Computing Machinery, New York, NY, USA, 458--467.
[28]
Samir Passi and Steven Jackson. 2017. Data Vision: Learning to See Through Algorithmic Abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). Association for Computing Machinery, New York, NY, USA, 2436--2447. https://doi.org/10.1145/2998181.2998331
[29]
Paul R Pintrich, Robert W Roeser, and Elisabeth AM De Groot. 1994. Classroom and individual differences in early adolescents' motivation and self-regulated learning. The Journal of Early Adolescence 14, 2 (1994), 139--161.
[30]
Jan L Plass, Bruce D Homer, and Charles K Kinzer. 2015. Foundations of game-based learning. Educational Psychologist 50, 4 (2015), 258--283.
[31]
Phil Sands, Aman Yadav, and Jon Good. 2018. Computational thinking in K-12: In-service teacher perceptions of computational thinking. In Computational thinking in the STEM disciplines. Springer, 151--164.
[32]
Purvi Saraiya, Clifford A. Shaffer, D. Scott McCrickard, and Chris North. 2004. Effective Features of Algorithm Visualizations. SIGCSE Bull. 36, 1 (March 2004), 382--386. https://doi.org/10.1145/1028174.971432
[33]
Clifford A. Shaffer, Matthew L. Cooper, Alexander Joel D. Alon, Monika Akbar, Michael Stewart, Sean Ponce, and Stephen H. Edwards. 2010. Algorithm Visualization: The State of the Field. ACM Trans. Comput. Educ. 10, 3, Article 9 (2010), 22 pages.
[34]
Lloyd Shapley and Herbert Scarf. 1974. On cores and indivisibility. Journal of Mathematical Economics 1, 1 (1974), 23--37. https://doi.org/10.1016/0304-4068(74)90033-0
[35]
Elizabeth Simpson. 1971. Educational objectives in the psychomotor domain. Be- havioral Objectives in Curriculum Development: Selected Readings and Bibliography 60, 2 (1971).
[36]
David Scot Taylor, Andrei F. Lurie, Cay S. Horstmenn, Menko B. Johnson, Sean K. Sharma, and Edward C. Yin. 2009. Predictive vs. Passive Animation Learning Tools. In Proceedings of the 40th ACM Technical Symposium on Computer Science Education (Chattanooga, TN, USA) (SIGCSE '09). Association for Computing Machinery, New York, NY, USA, 494--498. https://doi.org/10.1145/1508865.1509038
[37]
Jaime Urquiza-Fuentes and J. Ángel Velázquez-Iturbide. 2009. A Survey of Successful Evaluations of Program Visualization and Algorithm Animation Systems. ACM Trans. Comput. Educ. 9, 2, Article 9 (June 2009), 21 pages. https://doi.org/10.1145/1538234.1538236
[38]
Lisa Vangsness and Michael E Young. 2020. More isn't always better: when metacognitive prompts are misleading. Metacognition and Learning (2020), 1--22.
[39]
Peng Wang, Roman Bednarik, and Andrés Moreno. 2012. During Automatic Program Animation, Explanations after Animations Have Greater Impact than before Animations. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling '12). Association for Computing Machinery, New York, NY, USA, 100--109. https://doi.org/10. 1145/2401796.2401808
[40]
Qiaosi Wang, Shan Jing, Ida Camacho, David Joyner, and Ashok Goel. 2020. Jill Watson SA: Design and Evaluation of a Virtual Agent to Build Communities Among Online Learners. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI EA '20). Association for Computing Machinery, New York, NY, USA, 1--8. https://doi.org/10.1145/3334480.3382878
[41]
Jeannette M Wing. 2006. Computational thinking. Commun. ACM 49, 3 (2006), 33--35.
[42]
Soojeong Yoo, Sunkyung Kim, and Youngho Lee. 2020. Learning by Doing: Evaluation of an Educational VR Application for the Care of Schizophrenic Patients. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA '20). Association for Computing Machinery, New York, NY, USA, 1--6. https://doi.org/10.1145/3334480.338285151

Cited By

View all
  • (2023)The Robots Are Here: Navigating the Generative AI Revolution in Computing EducationProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633499(108-159)Online publication date: 22-Dec-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1
March 2023
1481 pages
ISBN:9781450394314
DOI:10.1145/3545945
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: 03 March 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. computational thinking
  2. interactive learning
  3. skill acquisition

Qualifiers

  • Research-article

Funding Sources

Conference

SIGCSE 2023
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)219
  • Downloads (Last 6 weeks)26
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2023)The Robots Are Here: Navigating the Generative AI Revolution in Computing EducationProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633499(108-159)Online publication date: 22-Dec-2023

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media