research-article

PDL: Scaffolding Problem Solving in Programming Courses

Authors:

Wenxin LiAuthors Info & Claims

ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1

Pages 185 - 191

https://doi.org/10.1145/3430665.3456360

Published: 26 June 2021 Publication History

Abstract

Programming tasks provide an opportunity for students to improve their problem-solving skills (PSS). However, when programming tasks are challenging, students could become demotivated and lose the opportunity to improve PSS in the process. To scaffold the difficulty of programming tasks and better motivate students to enhance PSS via coding, this paper introduces PDL (Problem Description Language). Given the natural-language description of a combinatorial optimization problem (COP), PDL requires students to describe (i) inputs, (ii) constraints, (iii) the optimization objective, and (iv) outputs, based on their problem comprehension. PDL then validates each problem description by (1) compiling a solution program from the description and (2) executing the generated program with predefined test cases. Based on the compiling and testing results, PDL provides feedback to students, and assists students to adjust their problem comprehension and improve problem descriptions.

To evaluate PDL's effectiveness in motivating students to fulfill challenging programming tasks, we conducted a user study with 185 undergraduates and asked the students to solve COPs with or without PDL. We found that the students with PDL were less likely to give up than students without PDL. By using PDL, students solved more COPs and spent less time on each problem; they became more confident and motivated in handling COPs after using PDL.

References

[1]

I.E. Allen and C.A. Seaman. 2007. Likert scales and data analyses. Quality Progress 40 (07 2007), 64--65.

[2]

A. Bart, J. Tibau, E. Tilevich, C. A. Shaffer, and D. Kafura. 2017. BlockPy: An Open Access Data-Science Environment for Introductory Programmers. Computer 50, 05 (may 2017), 18--26. https://doi.org/10.1109/MC.2017.132

[3]

Theresa Beaubouef and John Mason. 2005. Why the High Attrition Rate for Computer Science Students: Some Thoughts and Observations. SIGCSE Bull. 37, 2 (June 2005), 103--106. https://doi.org/10.1145/1083431.1083474

Digital Library

[4]

Pietro Belotti, Sonia Cafieri, Jon Lee, and Leo Liberti. 2010. Feasibility-based bounds tightening via fixed points. In International Conference on Combinatorial Optimization and Applications. Springer, 65--76.

[5]

Matthew Butler and Michael Morgan. 2007. Learning challenges faced by novice programming students studying high level and low feedback concepts. In Proceedings of ASCILITE - Australian Society for Computers in Learning in Tertiary Education Annual Conference 2007. Australasian Society for Computers in Learning in Tertiary Education, 99--107. https://www.learntechlib.org/p/46043

[6]

William J. Cook, William H. Cunningham, William R. Pulleyblank, and Alexander Schrijver. 1998. Combinatorial Optimization. John Wiley & Sons, Inc., New York, NY, USA.

[7]

Michael De Raadt. 2008.Teaching programming strategies explicitly to novice programmers. Ph.D. Dissertation.

[8]

Christian DeLozier, Richard Eisenberg, Santosh Nagarakatte, Peter-Michael Osera, Milo Martin, and S. Zdancewic. 2013. Ironclad C++ A Library-Augmented Type-Safe Subset of C++. ACM SIGPLAN Notices 48, 287--304. https://doi.org/10.1145/2509136.2509550

Digital Library

[9]

Hammond Ed. 2001. Scaffolding: Teaching and Learning in Language and Literacy Education. (01 2001).

[10]

Francisco Buitrago Flórez, Rubby Casallas, Marcela Hernández, Alejandro Reyes, Silvia Restrepo, and Giovanna Danies. 2017. Changing a Generation's Way of Thinking: Teaching Computational Thinking Through Programming. Review of Educational Research 87, 4 (2017), 834--860. https://doi.org/10.3102/0034654317710096 arXiv: https://doi.org/10.3102/0034654317710096

[11]

Wellesley Foshay and Jamie Kirkley. 1998. Principles for Teaching Problem Solving.

[12]

Felienne Hermans and Efthimia Aivaloglou. 2017. To Scratch or Not to Scratch? A Controlled Experiment Comparing Plugged First and Unplugged First Programming Lessons. In Proceedings of the 12th Workshop on Primary and Secondary Computing Education(Nijmegen, Netherlands)(WiPSCE '17). Association for Computing Machinery, New York, NY, USA, 49--56. https://doi.org/10.1145/3137065.3137072

Digital Library

[13]

Mohd Ismail, Nor Ngah, and Irfan Umar. 2010. Instructional strategy in the teaching of computer programming: A need assessment analyses.The Turkish Online J Edu Technol 9 (04 2010).

[14]

Mohd Nasir Ismail, Nor Azilah Binti Ngah, and Irfan Naufal Umar. 2010. INSTRUCTIONAL STRATEGY IN THE TEACHING OF COMPUTER PROGRAMMING: A NEED ASSESSMENT ANALYSES.

[15]

Andrew J. Ko, Thomas D. LaToza, Stephen Hull, Ellen A. Ko, William Kwok,Jane Quichocho, Harshitha Akkaraju, and Rishin Pandit. 2019. Teaching Explicit Programming Strategies to Adolescents. 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, 7 pages. https://doi.org/10.1145/3287324.3287371

Digital Library

[16]

Thomas D. LaToza, Maryam Arab, Dastyni Loksa, and Amy J. Ko. 2020. Explicit Programming Strategies. Empirical Software Engineering(2020), 1--34.

[17]

Irene Lee, Fred Martin, Jill Denner, Bob Coulter, Walter Allan, Jeri Erickson, Joyce Malyn-Smith, and Linda Werner. 2011. Computational thinking for youth in practice. ACM Inroads2 (02 2011), 32--37. https://doi.org/10.1145/1929887.1929902

[18]

Dastyni Loksa, Andrew J. Ko, Will Jernigan, Alannah Oleson, and Margaret M.Burnett. 2016. Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance. Inthe 2016 CHI Conference.

[19]

John Maloney, Mitchel Resnick, Natalie Rusk, Brian Silverman, and Evelyn East-mond. 2010. The Scratch Programming Language and Environment. ACM Trans. Comput. Educ.10, 4, Article 16 (Nov. 2010), 15 pages. https://doi.org/10.1145/1868358.1868363

Digital Library

[20]

MIT App Inventor 2020. MIT App Inventor. https://appinventor.mit.edu.

[21]

PDLManual 2020. PDL Manual. Retrieved January 7, 2020 from https://github.com/pdlteam/pdl-manual/blob/master/pdlmanual.pdf

[22]

Frank Pfenning. 2010. C0 Reference. https://www.cs.cmu.edu/~fp/courses/15122-f10/misc/c0-reference.pdf.

[23]

Alex Ruthmann, Jesse M. Heines, Gena R. Greher, Paul Laidler, and Charles Saulters. 2010. Teaching Computational Thinking through Musical Live Codingin Scratch. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education(Milwaukee, Wisconsin, USA) (SIGCSE '10). Association for Computing Machinery, New York, NY, USA, 351--355. https://doi.org/10.1145/1734263.1734384

Digital Library

[24]

Shima Salehi, Karen D. Wang, Ruqayya Toorawa, and Carl Wieman. 2020. Can Majoring in Computer Science Improve General Problem-Solving Skills?. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education(Portland, OR, USA)(SIGCSE '20). Association for Computing Machinery, New York, NY, USA, 156--161. https://doi.org/10.1145/3328778.3366808

Digital Library

[25]

Jho-Ju Tu and John R. Johnson. 1990. Can Computer Programming Improve Problem-Solving Ability? SIGCSE Bull. 22, 2 (June 1990), 30--33. https://doi.org/10.1145/126445.126451

Digital Library

Cited By

Liu JGoodwin EFranklin DStone JYuen TShoop LRebelsky SPrather J(2025)Student Utilization of Metacognitive Strategies in Solving Dynamic Programming ProblemsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701888(687-693)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701888
Hao QLiu RStone JYuen TShoop LRebelsky SPrather J(2025)Towards Integrating Behavior-Driven Development in Mobile Development: An Experience ReportProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701875(450-456)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701875
Rocha HCosta ETedesco P(2023)Helping to provide adaptive feedback to novice programmers: a framework to assist the Teachers2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10212000(1-6)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10212000
Show More Cited By

Index Terms

PDL: Scaffolding Problem Solving in Programming Courses
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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cover image ACM Conferences

ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1

June 2021

611 pages

ISBN:9781450382144

DOI:10.1145/3430665

General Chairs:
Carsten Schulte
Paderborn University, Germany
,
Brett A. Becker
University College Dublin, Ireland
,
Program Chairs:
Monica Divitini
NTNU, Norway
,
Erik Barendsen
Radboud University & Open University, The Netherlands

Copyright © 2021 ACM.

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Published: 26 June 2021

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Science and Technology Innovation 2030 The New Generation of Artificial Intelligence Major Project

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ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education

June 26 - July 1, 2021

Virtual Event, Germany

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

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Cited By

Liu JGoodwin EFranklin DStone JYuen TShoop LRebelsky SPrather J(2025)Student Utilization of Metacognitive Strategies in Solving Dynamic Programming ProblemsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701888(687-693)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701888
Hao QLiu RStone JYuen TShoop LRebelsky SPrather J(2025)Towards Integrating Behavior-Driven Development in Mobile Development: An Experience ReportProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701875(450-456)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701875
Rocha HCosta ETedesco P(2023)Helping to provide adaptive feedback to novice programmers: a framework to assist the Teachers2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10212000(1-6)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10212000
Joseph ASarma SShelly Kumar V(2023)Bug-eecha 2.0: An Educational Game for CS1 Students and InstructorsProceedings of the 16th Annual ACM India Compute Conference10.1145/3627217.3627236(61-65)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3627217.3627236
Pawagi MKumar V(2023)GuardRails: Automated Suggestions for Clarifying Ambiguous Purpose StatementsProceedings of the 16th Annual ACM India Compute Conference10.1145/3627217.3627234(55-60)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3627217.3627234
Joyse Barbosa Rocha Hde Barros Costa ECabral de Azevedo Restelli Tedesco P(2023)A Taxonomy to Assist TAs in Providing Adaptive Feedback to Novice Programmers2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343309(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343309
De Souza JFlavell MSuleiman AShepherd DDeWaters JSmall MLiu YHou D(2023)The Importance of Project-Scale Scaffolding for Retention and Experience in Computing Courses2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343225(01-09)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343225
Sadik OOttenbreit-Leftwich A(2023)Understanding U.S. secondary computer science teachers’ challenges and needsComputer Science Education10.1080/08993408.2023.220947434:2(252-284)Online publication date: 4-May-2023
https://doi.org/10.1080/08993408.2023.2209474
Adnan ARomli R(2022)A Comparative Evaluation on Methods of Teaching Computer ProgrammingAdvances on Intelligent Informatics and Computing10.1007/978-3-030-98741-1_47(571-582)Online publication date: 30-Mar-2022
https://doi.org/10.1007/978-3-030-98741-1_47

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