research-article

Learning by Taking Apart: Deconstructing Code by Reading, Tracing, and Debugging

Author:

Jean M. GriffinAuthors Info & Claims

SIGITE '16: Proceedings of the 17th Annual Conference on Information Technology Education

Pages 148 - 153

https://doi.org/10.1145/2978192.2978231

Published: 28 September 2016 Publication History

Abstract

This theoretical paper discusses several lines of research which support the premise that people learning to program can do so more effectively and efficiently if they spend as much time deconstructing code (reading, tracing, and debugging) as they do writing code. This work builds upon research in computing education on reading and tracing code, and in education psychology on learning from worked-examples and errors. A graphical model is included of cognitive science principles that scaffold the process of learning to solve problems. A sample learning progression is provided for teachers and instructional designers. The progression begins with low-stakes deconstructionist activities such as exploring, identifying, comparing, and debugging, before activities that require writing code. Deconstructionism is discussed as a pedagogy and learning theory complementary to Seymour Papert's Constructionism.

References

[1]

Abelson, H. 2009. App Inventor for Android. Google Research Blog.

[2]

Adams, D.M., McLaren, B.M., Durkin, K., Mayer, R.E., Rittle-Johnson, B., Isotani, S. and van Velsen, M. 2014. Using erroneous examples to improve mathematics learning with a web-based tutoring system. Computers in Human Behavior. 36, (2014), 401--411.

Digital Library

[3]

Barker, L. and Hovey, C.L. 2014. Results of a Large-Scale, Multi-Institutional Study of Undergraduate Retention in Computing. Frontiers in Education (2014), 1--8.

[4]

Bell, T., Alexander, J., Freeman, I. and Grimley, M. 2009. Computer Science Unplugged: school students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology. 13, 1 (2009), 20--29.

[5]

Booth, J.L., Lange, K.E., Koedinger, K.R. and Newton, K.J. 2013. Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction. 25, (2013), 24--34.

[6]

Booth, J.L., McGinn, K.M., Barbieri, C., Begolli, K.N., Chang, B., Miller-Cotto, D., Young, L.K. and Davenport, J.L. 2016. Evidence for Cognitive Science Principles that Impact Learning in Mathematics. Cognitive Science and Mathematics (in press).

[7]

Booth, J.L., McGinn, K.M., Young, L.K. and Barbieri, C. 2015. Simple Practice Doesn't Always Make Perfect: Evidence From the Worked Example Effect. Policy Insights from the Behavioral and Brain Sciences. 2, 1 (2015), 24--32.

[8]

du Boulay, B., O'Shea, T. and Monk, J. 1981. The black box inside the glass box: presenting computing concepts to novices. International Journal of Man-Machine Studies. 14, (1981), 237--249.

[9]

Braught, G., Wahls, T. and Eby, L.M. 2011. The Case for Pair Programming in the Computer Science Classroom. ACM Transactions on Computing Education.

Digital Library

[10]

Brennan, K., Balch, C. and Chung, M. Creative Computing. Harvard Graduate School of Education.

[11]

Bruner, J. 1966. Toward a theory of instruction. Harvard University Press.

[12]

Buechley, L. 2006. A construction kit for electronic textiles. 10th IEEE International Symposium on Wearable Computers (2006), 83--90.

[13]

Caspersen, M.E. and Bennedsen, J. 2007. Instructional Design of a Programming Course -- A Learning Theoretic Approach. (2007), 111--122.

Digital Library

[14]

Catrambone, R. 1998. The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General. 127, 4 (1998), 355--376.

[15]

Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P. and Glaser, R. 1989. Self-Explanations: How students study and use examples in learning to solve problems. Cognitive Science. 13, (1989), 145--182.

[16]

Chi, M.T.H., De Leeuw, N., Chiu, M.-H. and Lavancher, C. 1994. Eliciting Self-Explanations Improves Understanding. Cognitive Science. 18, 3 (1994), 439--477.

[17]

Cross, J.H., Hendrix, T.D. and Barowski, L.A. 2011. Combining Dynamic Program Viewing and Testing in Early Computing Courses. Computer Software and Applications Conference IEEE 35th Annual (2011), 184--192.

Digital Library

[18]

csunplugged.org: http://csunplugged.org/.

[19]

Cuny, J. 2012. Transforming high school computing. ACM Inroads. 3, 2 (2012), 32.

Digital Library

[20]

DiSalvo, B., Yardi, S., Guzdial, M., McKlin, T., Meadows, C., Perry, K. and Bruckman, A. 2011. African American Males Constructing Computing Identity. Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems - CHI '11. (2011), 2967--2970.

Digital Library

[21]

Fincher, S. and Petre, M. eds. 2004. Computer Science Education Research. Psychology Press.

Digital Library

[22]

Fitzgerald, S., Lewandowski, G., McCauley, R., Murphy, L., Simon, B., Thomas, L. and Zander, C. 2008. Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education.

[23]

Ginat, D. 2008. Learning from Wrong and Creative Algorithm Design. ACM SIGCSE Bulletin. 40, 1 (2008), 26--30.

Digital Library

[24]

Ginat, D. and Shmallo, R. 2013. Constructive Use of Errors in Teaching CS1. Proceeding of the 44th ACM technical symposium on Computer science education (SIGCSE 2013) (2013), 353--358.

Digital Library

[25]

Gray, S., Clair, C.S., James, R., Park, W. and Mead, J. 2007. Suggestions for Graduated Exposure to Programming Concepts Using Fading Worked Examples. Proceedings of the third International Workshop on Computing Education Research (ICER '07) (2007), 99--110.

Digital Library

[26]

Griffin, J., Kaplan, E. and Burke, Q. 2012. Debug-ems and Other Deconstruction Kits for STEM learning. Integrated STEM Education Conference (ISEC), IEEE 2nd (2012), 1--4.

[27]

Große, C.S. and Renkl, A. 2007. Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction. 17, 6 (Dec. 2007), 612--634.

[28]

Guzdial, M. 2015. Learner-Centered Design of Computing Education: Research on Computing for Everyone. Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool. 1--165.

[29]

Harel, I.R. 1988. Software Design for Learning: Children's Construction of Meaning for Fractions and LOGO Programming (Doctoral Dissertation). MIT.

[30]

Hu, H. and Shepherd, T.D. 2013. Using POGIL to Help Students Learn to Program. ACM Transactions on Computing Education (TOCE). 13, 3 (2013).

Digital Library

[31]

Jenkins, T. 2002. On the Difficulty of Learning to Program. Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences. 4, (2002), 53--58.

[32]

Johnson, W.L. 1990. Understanding and debugging novice programs. Artificial Intelligence. 42, 1 (1990), 51--97.

Digital Library

[33]

Kafai, Y.B., Lee, E., Searle, K., Fields, D., Kaplan, E. and Lui, D. 2014. A crafts-oriented approach to computing in high school: Introducing computational concepts, practices, and perspectives with electronic textiles. ACM Transactions on Computing Education. 14, 1 (2014), 1--20.

Digital Library

[34]

Katz, A. and Shmallo, R. 2015. Improving Relational Data Modelling Through Learning From Errors. Proceedings of the IADIS Multi Conference of Computer Science and Information Systems (MCCSIS 2015), Volume: Theory and Practice in Modern Computing TPMC. (2015), 198--202.

[35]

Katz, I. and Anderson, J. 1987. Debugging: An Analysis of Bug-Location Strategies. Human-Computer Interaction. 3, 4 (1987), 351--399.

Digital Library

[36]

Kick, R. and Trees, F.P. 2015. AP CS Principles: Engaging, Challenging, and Rewarding. ACM Inroads. 6, 1 (2015), 42--45.

Digital Library

[37]

Klahr, D. and Carver, S.M. 1988. Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology. 20, 3 (1988), 362--404.

[38]

Kussmaul, C. 2012. Process Oriented Guided Inquiry Learning (POGIL) for Computer Science. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (2012), 373--378.

Digital Library

[39]

Lahtinen, E., Ala-Mutka, K. and Järvinen, H.-M. 2005. A study of the difficulties of novice programmers. Proceedings of the 10th annual conference on Innovation and Technology in Computer Science Education. (2005), 14--18.

Digital Library

[40]

Lawrance, J., Bogart, C., Burnett, M., Bellamy, R., Rector, K. and Fleming, S.D. 2013. How programmers debug, revisited: An information foraging theory perspective. IEEE Transactions on Software Engineering. 39, 2 (2013), 197--215.

Digital Library

[41]

Lee, G.C. and Wu, J.C. 1999. Debug It: A debugging practicing system. Computers & Education. 32, 2 (1999), 165--179.

Digital Library

[42]

Lee, M.J. 2015. Teaching and Engaging with Debugging Puzzles (Doctoral Dissertation). University of Washington.

[43]

Lieberman, H. 1997. The Debugging Scandal and What to Do About It. Communications of the ACM. 40, 4 (1997), 26--78.

Digital Library

[44]

Lister, R., Adams, E.S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J.E., Sanders, K., Seppälä, O., Simon, B. and Thomas, L. 2004. A multi-national study of reading and tracing skills in novice programmers. ACM SIGCSE Bulletin. 36, 4 (2004), 119.

Digital Library

[45]

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

Digital Library

[46]

Lopez, M., Whalley, J., Robbins, P. and Lister, R. 2008. Relationships between reading, tracing and writing skills in introductory programming. Proceedings of the fourth International Workshop on Computing Education Research (ICER '08) (2008), 101--112.

Digital Library

[47]

Maloney, J., Resnick, M., Rusk, N., Silverman, B. and Eastmond, E. 2010. The scratch programming language and environment. ACM Transactions on Computing Education (TOCE). 10, 4 (2010), 16.

Digital Library

[48]

Margolis, J., Estrella, R., Goode, J. and Nao, K. 2008. Stuck in the shallow end: Education, race, and computing. The MIT Press.

Digital Library

[49]

Margolis, J. and Fisher, A. 2003. Unlocking the clubhouse: Women in computing. The MIT press.

[50]

Margulieux, L.E., Catrambone, R. and Guzdial, M. 2016. Employing subgoals in computer programming education. Computer Science Education. (2016), 1--24.

[51]

Mazur, E. 1997. Peer instruction. Prentice-Hall.

[52]

McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L. and Zander, C. 2008. Debugging: a review of the literature from an educational perspective. Computer Science Education. 18, April 2015 (2008), 67--92.

[53]

McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Ben-David Kolikant, Y., Laxer, C., Thomas, L., Utting, I. and Wilusz, T. 2001. A Multi-National, Multi-Institutional Study of Assessment of Programming Skills of First-Year CS Students. SIGCSE Bulletin. 33, 4 (2001), 125--180.

Digital Library

[54]

McLaren, B.M., Adams, D.M. and Mayer, R.E. 2015. Delayed Learning Effects with Erroneous Examples: A Study of Learning Decimals with a Web-Based Tutor. International Journal of Artificial Intelligence in Education. 25, 4 (2015), 520--542.

[55]

Moog, R.S. and Spencer, J.N. 2008. Process-Oriented Guided Inquiry Learning (POGIL). Oxford University Press.

[56]

Morrison, B.B., Margulieux, L.E. and Street, C. 2015. Subgoals, Context, and Worked Examples in Learning Computing Problem Solving. Proceedings of the eleventh International Conference on Computing Education Research (ICER '15) (2015), 21--29.

Digital Library

[57]

Ohlsson, S. 1996. Learning from error and the design of task environments. International Journal of Educational Research. 25, 5 (1996), 419--448.

[58]

Paas, F., Renkl, A. and Sweller, J. 2004. Cognitive Load Theory: Instructional Implications of the Interaction between Information Structures and Cognitive Architecture. Instructional Science. 32, 1 (2004), 1--8.

[59]

Papert, S. 1980. Mindstorms: children, computers, and powerful ideas. Basic Books, Inc.

Digital Library

[60]

Patitsas, E., Craig, M. and Easterbrook, S. 2013. Comparing and Contrasting Different Algorithms Leads to Increased Student Learning. Proceedings of the 9th International Conference on Computing Education Research (ICER '13). (2013), 145--152.

Digital Library

[61]

Pirolli, P. 1993. Effects of Examples and Their Explanations in a Lesson on Recursion: A Production System Analysis. Cognition and Instruction. 8, 3 (1993), 207--259.

[62]

Pirolli, P. and Recker, M. 1994. Learning Strategies and Transfer in the Domain of Programming. Cognition and Instruction. 12, 3 (1994), 235--275.

[63]

Porter, L., Bailey-Lee, C. and Simon, B. 2013. Halving Fail Rates using Peer Instruction?: A Study of Four Computer Science Courses. Proceeding of the 44th technical symposium on Computer science education (SIGCSE '13) (2013), 177--182.

Digital Library

[64]

Richards, B. 2000. Bugs as Features: Teaching Network Protocols Through Debugging. ACM SIGCSE Bulletin. (2000), 256--259.

Digital Library

[65]

Rittle-Johnson, B. and Star, J.R. 2009. Compared with what? The effects of different comparisons on conceptual knowledge and procedural flexibility for equation solving. Journal of Educational Psychology. 101, 3 (2009), 529--544.

[66]

Schworm, S. and Renkl, A. 2006. Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers and Education. 46, 4 (2006), 426--445.

Digital Library

[67]

Simon, B., Kohanfars, M., Lee, J., Tamayo, K. and Cutts, Q. 2010. Experience report: peer instruction in introductory computing. SIGCSE '10: Proceedings of the 41st ACM technical symposium on Computer science education (2010).

Digital Library

[68]

Skudder, B. and Luxton-Reilly, A. 2014. Worked Examples in Computer Science. Sixteenth Australasian Computing Education Conference (2014), 59--64.

Digital Library

[69]

Soloway, E. and Spohrer, J.C. eds. 1989. Studying the novice programmer. L. Erlbaum Associates.

Digital Library

[70]

Sorva, J. 2013. Notional machines and introductory programming education. ACM Transactions on Computing Education. 13, 2 (2013), 1--31.

Digital Library

[71]

Sorva, J., Karavirta, V. and Malmi, L. 2013. A review of generic program visualization systems for introductory programming education. Transactions on Computing Education. 13, 4 (2013).

Digital Library

[72]

Sweller, J. 1988. Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science. 12, 2 (1988), 257--285.

[73]

Sweller, J. and Cooper, G.A. 1985. The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra. Cognition and Instruction. 2, 1 (1985), 59--89.

[74]

Vygotsky, L.S. 1978. Mind in society: the development of higher psychological processes. Harvard University Press.

[75]

Whalley, J.L., Lister, R., Thompson, E., Clear, T., Robbins, P., Kumar, P.K. and Prasad, C. 2006. An Australasian Study of Reading and Comprehension Skills in Novice Programmers, using the Bloom and SOLO Taxonomies. Proceedings of the 8th Australasian Conference on Computing Education-Volume 52 (2006), 243--252.

Digital Library

[76]

Wood, D., Bruner, J.S. and Ross, G. 1976. The role of tutoring in problem-solving. Journal of Child Psychology and Psychiatry. 17, 2 (1976), 89--100.

[77]

Yoon, I., Kang, E. and Kwon, O. 2014. DeBugger Game: Mobile Virtual Lab for Introductory Computer Programming Courses. Proceedings of the 2014 American Society for Engineering Education Zone IV Conference (2014).

Cited By

Hennessy Elliott CNixon JGendrau Chakarov ABush JSchneider MRecker M(2024)Characterizing Teacher Support of Debugging with Physical Computing: Debugging Pedagogies in PracticeACM Transactions on Computing Education10.1145/367761224:4(1-28)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3677612
Cameron SPham TBagui SStephenson BStone JBattestilli LRebelsky SShoop L(2024)A Case Study of Taking AP Computer Science Principles: A Student's PerspectiveProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 210.1145/3626253.3635490(1588-1589)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3626253.3635490
Leinonen JDenny PMacNeil SSarsa SBernstein SKim JTran AHellas ALaakso MMonga MSimon Sheard J(2023)Comparing Code Explanations Created by Students and Large Language ModelsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588785(124-130)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588785
Show More Cited By

Index Terms

Learning by Taking Apart: Deconstructing Code by Reading, Tracing, and Debugging
1. Social and professional topics
  1. Professional topics
    1. Computing education

Recommendations

Exploring Active Learning Approaches to Computer Science Classes
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education

We present our experience in a Computer Science (CS) introductory course, where three teaching practices were implemented and compared: lectured-based learning, problem-based learning, and peer instruction. We chose Information Systems, a first-term ...
A constructionist learning environment for teachers to model learning designs

The use of digital technologies is now widespread and increasing, but is not always optimized for effective learning. Teachers in higher education have little time or support to work on innovation and improvement of their teaching, which often means ...
Improving Inquiry-Driven Modeling in Science Education through Interaction with Intelligent Tutoring Agents
IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

This paper presents the design and evaluation of a set of intelligent tutoring agents constructed to teach teams of students an authentic process of inquiry-driven modeling. The paper first presents the theoretical grounding for inquiry-driven modeling ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGITE '16: Proceedings of the 17th Annual Conference on Information Technology Education

September 2016

188 pages

ISBN:9781450344524

DOI:10.1145/2978192

Conference Chair:
Deborah Boisvert
University of Massachusetts Boston, USA
,
Program Chair:
Stephen Zilora
Rochester Institute of Technology, USA

Copyright © 2016 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

SIGITE: ACM Special Interest Group on Information Technology Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGITE/RIIT 2016

Sponsor:

SIGITE

SIGITE/RIIT 2016: The 17th Annual Conference on Information Technology Education and the 5th Annual Conference on Research in Information Technology

September 28 - October 1, 2016

Massachusetts, Boston, USA

Acceptance Rates

SIGITE '16 Paper Acceptance Rate 26 of 67 submissions, 39%;

Overall Acceptance Rate 176 of 429 submissions, 41%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
599
Total Downloads

Downloads (Last 12 months)76
Downloads (Last 6 weeks)7

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hennessy Elliott CNixon JGendrau Chakarov ABush JSchneider MRecker M(2024)Characterizing Teacher Support of Debugging with Physical Computing: Debugging Pedagogies in PracticeACM Transactions on Computing Education10.1145/367761224:4(1-28)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3677612
Cameron SPham TBagui SStephenson BStone JBattestilli LRebelsky SShoop L(2024)A Case Study of Taking AP Computer Science Principles: A Student's PerspectiveProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 210.1145/3626253.3635490(1588-1589)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3626253.3635490
Leinonen JDenny PMacNeil SSarsa SBernstein SKim JTran AHellas ALaakso MMonga MSimon Sheard J(2023)Comparing Code Explanations Created by Students and Large Language ModelsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588785(124-130)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588785
MacNeil STran AHellas AKim JSarsa SDenny PBernstein SLeinonen JDoyle MStephenson BDorn BSoh LBattestilli L(2023)Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-BookProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569785(931-937)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569785
Kapoor AXie TKwan LGardner-Mccune CDoyle MStephenson BDorn BSoh LBattestilli L(2023)Logistics, Affordances, and Evaluation of Build ProgrammingProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569756(179-185)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569756
Kim CVasconcelos LBelland BUmutlu DGleasman C(2022)Debugging behaviors of early childhood teacher candidates with or without scaffoldingInternational Journal of Educational Technology in Higher Education10.1186/s41239-022-00319-919:1Online publication date: 10-Mar-2022
https://doi.org/10.1186/s41239-022-00319-9
Bofferding LKocabas SAqazade MHaiduc AChen L(2022)The effect of play and worked examples on first and third graders' creating and debugging of programming algorithmsComputational Thinking in PreK-510.1145/3507951.3519284(19-29)Online publication date: 15-Jan-2022
https://dl.acm.org/doi/10.1145/3507951.3519284
Eriksson EBaykal GTorgersson O(2022)The Role of Learning Theory in Child-Computer Interaction - A Semi-Systematic Literature ReviewProceedings of the 21st Annual ACM Interaction Design and Children Conference10.1145/3501712.3529728(50-68)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3501712.3529728
Huang JParker M(2022)Developing computational thinking collaboratively: the nexus of computational practices within small groupsComputer Science Education10.1080/08993408.2022.203948833:3(342-374)Online publication date: 3-Apr-2022
https://doi.org/10.1080/08993408.2022.2039488
Theodoropoulos A(2022)Participatory design and participatory debugging: Listening to students to improve computational thinking by creating gamesInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2022.10052534(100525)Online publication date: Dec-2022
https://doi.org/10.1016/j.ijcci.2022.100525
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