skip to main content
10.1145/3300115.3309504acmconferencesArticle/Chapter ViewAbstractPublication PagescompedConference Proceedingsconference-collections
research-article

The Effect of Reading Code Aloud on Comprehension: An Empirical Study with School Students

Published:09 May 2019Publication History

ABSTRACT

In recent times, programming is increasingly taught to younger students in schools. While learning programming is known to be difficult, we can lighten the learning experience of this age group by adopting pedagogies that are common to them, but not as common in CS education. One of these pedagogies is Reading Aloud (RA), a familiar strategy when young children and beginners start learning how to read in their natural language. RA is linked with a better comprehension of text for beginner readers. We hypothesize that reading code aloud during introductory lessons will lead to better code comprehension. To this end, we design and execute a controlled experiment with the experimental group participants reading the code aloud during the lessons. The participants are 49 primary school students between 9 and 13 years old, who follow three lessons in programming in Python. The lessons are followed by a comprehension assessment based on Bloom's taxonomy. The results show that the students of the experimental group scored significantly higher in the Remembering-level questions compared to the ones in the control group. There is no significant difference between the two groups in their answers to the Understanding-level questions. Furthermore, the participants in both groups followed some of the instructed vocalizations more frequently such as the variable's assignment (is). Vocalizing the indentation spaces in a for -loop was among the least followed. Our paper suggests that using RA for teaching programming in schools will contribute to improving code comprehension with its effect on syntax remembering.

References

  1. Lorin W Anderson and David Krathwohl. 2001. A Taxonomy for Learning, Teaching, and Assessing. New York.Google ScholarGoogle Scholar
  2. Richard Arends. 2012. Learning to teach. McGraw-Hill.Google ScholarGoogle Scholar
  3. Craig Barton. 2018. How I wish I'd taught maths. John Catt Educational Ltd.Google ScholarGoogle Scholar
  4. Andrew Begel. {n. d.}. Programming By Voice: A Domain-specific Application of Speech Recognition. ({n. d.}).Google ScholarGoogle Scholar
  5. A. Begel and S.L. Graham. {n. d.}. Spoken Programs. 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05) ({n. d.}). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Begel and S.L. Graham. 2006. An Assessment of a Speech-Based Programming Environment. Visual Languages and Human-Centric Computing (VL/HCC'06) (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. B.S. Bloom. 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals. Number v. 1 in Taxonomy of Educational Objectives: The Classification of Educational Goals. D. McKay. 64012369 https://books.google.nl/books?id=hos6AAAAIAAJGoogle ScholarGoogle Scholar
  8. B. Du Boulay. 1986. Some Difficulties of Learning to Program. Journal of Educational Computing Research, Vol. 2, 1 (1986), 57--73. https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9Google ScholarGoogle ScholarCross RefCross Ref
  9. Linda E. Martin and Sherry Kragler. 2011. Becoming a Self-Regulated Reader: A Study of Primary-Grade Students' Reading Strategies. Literacy Research and Instruction, Vol. 50, 2 (2011), 89--104.Google ScholarGoogle ScholarCross RefCross Ref
  10. Michael F. Mascolo. 2015. Neo-Piagetian Theories of Cognitive Development. International Encyclopedia of the Social & Behavioral Sciences (2015), 501--510.Google ScholarGoogle Scholar
  11. R. Steve McCallum, Shannon Sharp, Sherry Mee Bell, and Thomas George. 2004. Silent versus oral reading comprehension and efficiency. Psychology in the Schools, Vol. 41, 2 (2004), 241--246.Google ScholarGoogle ScholarCross RefCross Ref
  12. Chris Parnin. 2011. Subvocalization - Toward Hearing the Inner Thoughts of Developers. 2011 IEEE 19th International Conference on Program Comprehension (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Perrone-Bertolotti, L. Rapin, J.-P. Lachaux, M. Baciu, and H. Lavenbruck. 2014. What is that little voice inside my head? Inner speech phenomenology, its role in cognitive performance, and its relation to self-monitoring. Behavioural Brain Research, Vol. 261 (2014), 220--239.Google ScholarGoogle ScholarCross RefCross Ref
  14. David E Price, DA Dahlstrom, Ben Newton, and Joseph L Zachary. 2002. Off to See the Wizard: using a "Wizard of Oz" study to learn how to design a spoken language interface for programming. In Frontiers in Education, 2002. FIE 2002. 32nd Annual, Vol. 1. IEEE, T2G--T2G.Google ScholarGoogle ScholarCross RefCross Ref
  15. Suzanne M Prior and Katherine A Welling. 2001. "Read in Your Head": A Vygotskian Analysis of the Transition from Oral to Silent Reading. Reading Psychology, Vol. 22, 1 (2001), 1--15.Google ScholarGoogle ScholarCross RefCross Ref
  16. Katherine E. Purswell and Dee C. Ray. 2014. Research With Small Samples. Counseling Outcome Research and Evaluation, Vol. 5, 2 (2014), 116--126.Google ScholarGoogle ScholarCross RefCross Ref
  17. Keith Rayner, Barbara R. Foorman, Charles A. Perfetti, David Pesetsky, and Mark S. Seidenberg. 2002. How Should Reading be Taught? Scientific American, Vol. 286, 3 (2002), 84--91.Google ScholarGoogle ScholarCross RefCross Ref
  18. Lana Edwards Santoro, David J. Chard, Lisa Howard, and Scott K. Baker. 2008. Making the Very Most of Classroom Read-Alouds to Promote Comprehension and Vocabulary. The Reading Teacher, Vol. 61, 5 (2008), 396--408.Google ScholarGoogle ScholarCross RefCross Ref
  19. Marcel Schmeier and Ruud Bijman. 2017. Effectief rekenonderwijs op de basisschool 1st edition ed.). Uitgeverij Pica.Google ScholarGoogle Scholar
  20. Lindsey Snell and Mr Jim Cunningham. 2000. An investigation into programming by voice and development of a toolkit for writing voice-controlled applications. (2000).Google ScholarGoogle Scholar
  21. Osamu Takeuchi, Maiko Ikeda, and Atsushi Mizumoto. 2012. Reading Aloud Activity in L2 and Cerebral Activation. RELC Journal, Vol. 43, 2 (2012), 151--167.Google ScholarGoogle ScholarCross RefCross Ref
  22. Donna Teague and Raymond Lister. 2014. Longitudinal Think Aloud Study of a Novice Programmer. In Proceedings of the Sixteenth Australasian Computing Education Conference - Volume 148 (ACE '14). Australian Computer Society, Inc, Darlinghurst, Australia, Australia, 41--50. http://dl.acm.org/citation.cfm?id=2667490.2667495 Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Errol Thompson, Andrew Luxton-Reilly, Jacqueline L. Whalley, Minjie Hu, and Phil Robbins. 2008. Bloom's Taxonomy for CS Assessment. In Proceedings of the Tenth Conference on Australasian Computing Education - Volume 78 (ACE '08). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 155--161. http://dl.acm.org/citation.cfm?id=1379249.1379265 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jacqueline Whalley and Nadia Kasto. 2014. A qualitative think-aloud study of novice programmers' code writing strategies. Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14 (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jacqueline L. Whalley, Raymond Lister, Errol Thompson, Tony Clear, Phil Robbins, P. K. Ajith Kumar, and Christine Prasad. 2006. An Australasian Study of Reading and Comprehension Skills in Novice Programmers, Using the Bloom and SOLO Taxonomies. In Proceedings of the 8th Australasian Conference on Computing Education - Volume 52 (ACE '06). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 243--252. http://dl.acm.org/citation.cfm?id=1151869.1151901 Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The Effect of Reading Code Aloud on Comprehension: An Empirical Study with School Students

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          CompEd '19: Proceedings of the ACM Conference on Global Computing Education
          May 2019
          260 pages
          ISBN:9781450362597
          DOI:10.1145/3300115

          Copyright © 2019 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 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].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 May 2019

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          CompEd '19 Paper Acceptance Rate33of100submissions,33%Overall Acceptance Rate33of100submissions,33%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader