[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3587102.3588795acmconferencesArticle/Chapter ViewAbstractPublication PagesiticseConference Proceedingsconference-collections
research-article

Choosing a Didactic Basis for an Instructional Video: What Are the Implications For Novice Programmers?

Published: 30 June 2023 Publication History

Abstract

Much work on instructional videos in computing education focuses on the overall impact and technical aspects of videos, such as motivation and length. However, it might be significant how the underlying pedagogical theory, the didactic basis, determines the delivery of the content. We conducted a randomized experiment to investigate the research question: How does the didactic basis of an instructional video affect code writing performance and self-efficacy given the basic skill of novice programmers? Our data included two cohorts of 133 and 428 CS1 students from the Fall semesters of 2021/22 and 2022/23, respectively. In cohort 1, videos based on language-sensitive teaching led to significantly better results in writing code in object orientation for novices with medium basic skills than videos based on worked examples. This result could not be replicated in cohort 2. We found no effect on novice self-efficacy in either cohort.

References

[1]
David Andrich. 1978. Application of a psychometric rating model to ordered categories which are scored with successive integers. Applied psychological measurement, Vol. 2, 4 (1978), 581--594.
[2]
Jürgen Börstler, Henrik B. Christensen, Jens Bennedsen, Marie Nordström, Lena Kallin Westin, Jan Erik Moström, and Michael E. Caspersen. 2008. Evaluating OO Example Programs for CS1. SIGCSE Bull., Vol. 40, 3 (June 2008), 47--52. https://doi.org/10.1145/1597849.1384286
[3]
Jerome S. Bruner. 1967. Toward a Theory of Instruction. Belknap Press of Harvard University, Cambridge, Massachusetts. 66013179
[4]
Christine DeMars. 2010. Item response theory. Oxford University Press.
[5]
Yuemeng Du, Andrew Luxton-Reilly, and Paul Denny. 2020. A Review of Research on Parsons Problems. In Proceedings of the Twenty-Second Australasian Computing Education Conference (Melbourne, VIC, Australia) (ACE'20). ACM, New York, NY, USA, 195--202. https://doi.org/10.1145/3373165.3373187
[6]
Anna Eckerdal and Michael Thuné. 2005. Novice Java Programmers' Conceptions of "Object" and "Class", and Variation Theory. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (Caparica, Portugal) (ITiCSE '05). ACM, New York, NY, USA, 89--93. https://doi.org/10.1145/1067445.1067473
[7]
Kirstin Erath, Jenni Ingram, Judit Moschkovich, and Susanne Prediger. 2021. Designing and enacting instruction that enhances language for mathematics learning: A review of the state of development and research. ZDM--Mathematics Education, Vol. 53, 2 (2021), 245--262.
[8]
Svana Esche. 2022. Linking of Language and Programming and its Effects on Code Writing and Self-Efficacy in CS1. In 1. Nachwuchs-Konferenz der Didaktik der Informatik. Fachgruppe DDI der Gesellschaft für Informatik, 11--13.
[9]
Onyeka Ezenwoye. 2018. What Language? - The Choice of an Introductory Programming Language. In 2018 IEEE Frontiers in Education Conference (FIE). IEEE, San Jose, CA, USA, 1--8. https://doi.org/10.1109/FIE.2018.8658592
[10]
Ken Goldman, Paul Gross, Cinda Heeren, Geoffrey Herman, Lisa Kaczmarczyk, Michael C. Loui, and Craig Zilles. 2008. Identifying Important and Difficult Concepts in Introductory Computing Courses Using a Delphi Process. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE '08). ACM, New York, NY, USA, 256--260. https://doi.org/10.1145/1352135.1352226
[11]
Jamie Gorson and Eleanor O'Rourke. 2020. Why Do CS1 Students Think They're Bad at Programming? Investigating Self-Efficacy and Self-Assessments at Three Universities. In Proceedings of the 2020 ACM Conference on International Computing Education Research (Virtual Event, New Zealand) (ICER '20). ACM, New York, NY, USA, 170--181. https://doi.org/10.1145/3372782.3406273
[12]
Philip J. Guo, Juho Kim, and Rob Rubin. 2014. How Video Production Affects Student Engagement: An Empirical Study of MOOC Videos. In Proceedings of the First ACM Conference on Learning @ Scale Conference (Atlanta, Georgia, USA) (L@S '14). ACM, New York, NY, USA, 41--50. https://doi.org/10.1145/2556325.2566239
[13]
Petri Ihantola, Juho Leinonen, and Matti Rintala. 2020. Students' Preferences Between Traditional and Video Lectures: Profiles and Study Success. In Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling '20). ACM, New York, NY, USA, Article 29, 5 pages. https://doi.org/10.1145/3428029.3428561
[14]
Päivi Kinnunen and Beth Simon. 2012. My program is ok -- am I? Computing freshmen's experiences of doing programming assignments. Computer Science Education, Vol. 22, 1 (2012), 1--28. https://doi.org/10.1080/08993408.2012.655091
[15]
Paul A. Kirschner. 2017. Stop propagating the learning styles myth. Computers & Education, Vol. 106 (2017), 166--171. https://doi.org/10.1016/j.compedu.2016.12.006
[16]
Lisa L. Lacher, Albert Jiang, Yu Zhang, and Mark C. Lewis. 2018. Including Coding Questions in Video Quizzes for a Flipped CS1. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (Baltimore, Maryland, USA) (SIGCSE '18). ACM, New York, NY, USA, 574--579. https://doi.org/10.1145/3159450.3159504
[17]
Essi Lahtinen, Kirsti Ala-Mutka, and Hannu-Matti J"arvinen. 2005. A Study of the Difficulties of Novice Programmers. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (Caparica, Portugal) (ITiCSE '05). ACM, New York, NY, USA, 14--18. https://doi.org/10.1145/1067445.1067453
[18]
ACM Digital Library. 2023. Publication Date for the Query “instructional video novice”. https://tinyurl.com/m9xad82m
[19]
Alex Lishinski and Joshua Rosenberg. 2021. All the Pieces Matter: The Relationship of Momentary Self-Efficacy and Affective Experiences with CS1 Achievement and Interest in Computing. In Proceedings of the 17th ACM Conference on International Computing Education Research (Virtual Event, USA) (ICER 2021). ACM, New York, NY, USA, 252--265. https://doi.org/10.1145/3446871.3469740
[20]
Alex Lishinski, Aman Yadav, Jon Good, and Richard Enbody. 2016. Learning to Program: Gender Differences and Interactive Effects of Students' Motivation, Goals, and Self-Efficacy on Performance. In Proceedings of the 2016 ACM Conference on International Computing Education Research (Melbourne, VIC, Australia) (ICER '16). ACM, New York, NY, USA, 211--220. https://doi.org/10.1145/2960310.2960329
[21]
Andrew Luxton-Reilly, Simon, Ibrahim Albluwi, Brett A. Becker, Michail Giannakos, Amruth N. Kumar, Linda Ott, James Paterson, Michael James Scott, Judy Sheard, and Claudia Szabo. 2018. Introductory Programming: A Systematic Literature Review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (Larnaca, Cyprus) (ITiCSE 2018 Companion). ACM, New York, NY, USA, 55--106. https://doi.org/10.1145/3293881.3295779
[22]
Lauren E. Margulieux, Briana B. Morrison, and Adrienne Decker. 2019. Design and Pilot Testing of Subgoal Labeled Worked Examples for Five Core Concepts in CS1. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE '19). ACM, New York, NY, USA, 548--554. https://doi.org/10.1145/3304221.3319756
[23]
J. Patrick Meyer. 2014. Applied Measurement with jMetrik. Routledge, Florence.
[24]
Colin Moore, Lina Battestilli, and Ignacio X. Dom'inguez. 2021. Finding Video-Watching Behavior Patterns in a Flipped CS1 Course. ACM, New York, NY, USA, 768--774. https://doi.org/10.1145/3408877.3432359
[25]
Kasia Muldner, Jay Jennings, and Veronica Chiarelli. 2022. A Review of Worked Examples in Programming Activities. ACM Trans. Comput. Educ., Vol. 23, 1, Article 13 (dec 2022), 35 pages. https://doi.org/10.1145/3560266
[26]
Paul R. Pintrich, David A. F. Smith, Teresa Garcia, and Wilbert J. McKeachie. 1991. A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Technical Report. National Center for Research to improve Postsecondary Teaching and Learning, Ann Arbor.
[27]
Susanne Prediger and Lena Wessel. 2013. Fostering German-language learners' constructions of meanings for fractionstextemdashdesign and effects of a language- and mathematics-integrated intervention. Mathematics Education Research Journal, Vol. 25, 3 (jun 2013), 435--456. https://doi.org/10.1007/s13394-013-0079--2
[28]
Yizhou Qian and James Lehman. 2017. Students' Misconceptions and Other Difficulties in Introductory Programming: A Literature Review. ACM Trans. Comput. Educ., Vol. 18, 1, Article 1 (Oct. 2017), 24 pages. https://doi.org/10.1145/3077618
[29]
Adalbert Gerald Soosai Raj, Pan Gu, Eda Zhang, Arokia Xavier Annie R, Jim Williams, Richard Halverson, and Jignesh M. Patel. 2020. Live-Coding vs Static Code Examples: Which is Better with Respect to Student Learning and Cognitive Load?. In Proceedings of the Twenty-Second Australasian Computing Education Conference (Melbourne, VIC, Australia) (ACE'20). ACM, New York, NY, USA, 152--159. https://doi.org/10.1145/3373165.3373182
[30]
Vennila Ramalingam and Susan Wiedenbeck. 1998. Development and Validation of Scores on a Computer Programming Self-Efficacy Scale and Group Analyses of Novice Programmer Self-Efficacy. Journal of Educational Computing Research, Vol. 19, 4 (1998), 367--381. https://doi.org/10.2190/C670-Y3C8-LTJ1-CT3P
[31]
Guttorm Sindre. 2020. Code Writing vs Code Completion Puzzles: Analyzing Questions in an E-exam. In 2020 IEEE Frontiers in Education Conference (FIE). IEEE, 1--9. https://doi.org/10.1109/FIE44824.2020.9273919
[32]
Phil Steinhorst, Andrew Petersen, and Jan Vahrenhold. 2020. Revisiting Self-Efficacy in Introductory Programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research (Virtual Event, New Zealand) (ICER '20). ACM, New York, NY, USA, 158--169. https://doi.org/10.1145/3372782.3406281
[33]
Mohsen Tavakol and Reg Dennick. 2011. Making sense of Cronbach's alpha. International journal of medical education, Vol. 2 (2011), 53.
[34]
F. Boray Tek, Kristin S. Benli, and Ezgi Deveci. 2018. Implicit Theories and Self-Efficacy in an Introductory Programming Course. IEEE Transactions on Education, Vol. 61, 3 (2018), 218--225. https://doi.org/10.1109/TE.2017.2789183
[35]
Nanette Veilleux, Rebecca Bates, Cheryl Allendoerfer, Diane Jones, Joyous Crawford, and Tamara Floyd Smith. 2013. The Relationship between Belonging and Ability in Computer Science. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (Denver, Colorado, USA) (SIGCSE '13). ACM, New York, NY, USA, 65--70. https://doi.org/10.1145/2445196.2445220
[36]
Lev Semenovich Vygotsky. 1978. Mind in society: Development of higher psychological processes. Harvard university press.
[37]
Michael Whitney and Bryan Dallas. 2019. Captioning Online Course Videos: An Investigation into Knowledge Retention and Student Perception. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE '19). ACM, New York, NY, USA, 511--517. https://doi.org/10.1145/3287324.3287347
[38]
Stelios Xinogalos. 2015. Object-Oriented Design and Programming: An Investigation of Novices' Conceptions on Objects and Classes. ACM Trans. Comput. Educ., Vol. 15, 3, Article 13 (July 2015), 21 pages. https://doi.org/10.1145/2700519
[39]
V. K. Zaretskii. 2009. The Zone of Proximal Development: What Vygotsky Did Not Have Time to Write. Journal of Russian & East European Psychology, Vol. 47, 6 (2009), 70--93.
[40]
Daniel Zingaro. 2014. Peer Instruction Contributes to Self-Efficacy in CS1. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (Atlanta, Georgia, USA) (SIGCSE '14). ACM, New York, NY, USA, 373--378. https://doi.org/10.1145/2538862.2538878

Index Terms

  1. Choosing a Didactic Basis for an Instructional Video: What Are the Implications For Novice Programmers?

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1
June 2023
694 pages
ISBN:9798400701382
DOI:10.1145/3587102
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 the author(s) 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: 30 June 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. CS1
  2. Java
  3. instructional video
  4. programming

Qualifiers

  • Research-article

Funding Sources

  • Qualitätsoffensive Lehrerbildung

Conference

ITiCSE 2023
Sponsor:

Acceptance Rates

Overall Acceptance Rate 552 of 1,613 submissions, 34%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 93
    Total Downloads
  • Downloads (Last 12 months)31
  • Downloads (Last 6 weeks)1
Reflects downloads up to 01 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media