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The Progression of Students’ Ability to Work With Scope, Parameter Passing and Aliasing

Authors:

Filip Strömbäck,

Pontus Haglund,

Aseel Berglund,

Erik BerglundAuthors Info & Claims

ACE '23: Proceedings of the 25th Australasian Computing Education Conference

Pages 39 - 48

https://doi.org/10.1145/3576123.3576128

Published: 30 January 2023 Publication History

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Abstract

Students need the ability to reason about the behavior of programs when working with advanced concepts like concurrency and abstraction. To achieve this, students require core programming skills that allow them to trace and predict the outcome of a program. While previous research indicates that teachers cannot expect students to acquire all core programming skills after their introductory CS course, less is known of students’ progression in later years. In this study, we investigate 397 students’ ability to predict the outcome of short computer programs. The participants are from different programs and progressions in their studies. We find that students, regardless of program and year, struggle with predicting the outcome of short programs that require an accurate mental model of some less readily apparent concepts, such as references. Further, we discover that there is no significant improvement in the first three years. Finally, we propose further avenues of research to improve these learning outcomes.

References

[1]

Russ Abbott and Chengyu Sun. 2008. Abstraction Abstracted. In Proceedings of the 2nd International Workshop on The Role of Abstraction in Software Engineering (Leipzig, Germany) (ROA ’08). Association for Computing Machinery, New York, NY, USA, 23–30. https://doi.org/10.1145/1370164.1370171

Digital Library

[2]

Piraye Bayman and Richard E. Mayer. 1983. A Diagnosis of Beginning Programmers’ Misconceptions of BASIC Programming Statements. Commun. ACM 26, 9 (sep 1983), 677–679. https://doi.org/10.1145/358172.358408

Digital Library

[3]

Benedict Du Boulay. 1986. Some Difficulties of Learning to Program. Journal of Educational Computing Research 2, 1 (1986), 57–73. https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9 arXiv:https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9

[4]

Naomi R. Boyer, Sara Langevin, and Alessio Gaspar. 2008. Self Direction & Constructivism in Programming Education. In Proceedings of the 9th ACM SIGITE Conference on Information Technology Education (Cincinnati, OH, USA) (SIGITE ’08). ACM, New York, NY, USA, 89–94. https://doi.org/10.1145/1414558.1414585

Digital Library

[5]

A. T. Chamillard and Kim A. Braun. 2000. Evaluating Programming Ability in an Introductory Computer Science Course. In Proceedings of the Thirty-First SIGCSE Technical Symposium on Computer Science Education(Austin, Texas, USA) (SIGCSE ’00). Association for Computing Machinery, New York, NY, USA, 212–216. https://doi.org/10.1145/330908.331857

Digital Library

[6]

Sally Fincher, Johan Jeuring, Craig S. Miller, Peter Donaldson, Benedict du Boulay, Matthias Hauswirth, Arto Hellas, Felienne Hermans, Colleen Lewis, Andreas Mühling, Janice L. Pearce, and Andrew Petersen. 2020. Notional Machines in Computing Education: The Education of Attention. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (Trondheim, Norway) (ITiCSE-WGR ’20). Association for Computing Machinery, New York, NY, USA, 21–50. https://doi.org/10.1145/3437800.3439202

Digital Library

[7]

Kathi Fisler, Shriram Krishnamurthi, and Preston Tunnell Wilson. 2017. Assessing and Teaching Scope, Mutation, and Aliasing in Upper-Level Undergraduates. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 213–218. https://doi.org/10.1145/3017680.3017777

Digital Library

[8]

Pontus Haglund, Filip Strömbäck, and Linda Mannila. 2021. Understanding Students’ Failure to use Functions as a Tool for Abstraction – An Analysis of Questionnaire Responses and Lab Assignments in a CS1 Python Course. Informatics in Education 20, 4 (2021), 583–614. https://doi.org/10.15388/infedu.2021.26

[9]

Orit Hazzan. 2008. Reflections on Teaching Abstraction and Other Soft Ideas. SIGCSE Bull. 40, 2 (June 2008), 40–43. https://doi.org/10.1145/1383602.1383631

Digital Library

[10]

Yifat Ben-David Kolikant. 2004. Learning Concurrency as an Entry Point to the Community of Computer Science Practitioners. Journal of Computers in Mathematics and Science Teaching 23, 1(2004), 21–46. https://www.learntechlib.org/p/12871

[11]

Herman Koppelman and Betsy van Dijk. 2010. Teaching Abstraction in Introductory Courses. In Proceedings of the Fifteenth Annual Conference on Innovation and Technology in Computer Science Education (Bilkent, Ankara, Turkey) (ITiCSE ’10). Association for Computing Machinery, New York, NY, USA, 174–178. https://doi.org/10.1145/1822090.1822140

Digital Library

[12]

Barbara Liskov and John Guttag. 2000. Program Development in Java: Abstraction, Specification, and Object-Oriented Design (1st ed.). Addison-Wesley Longman Publishing Co., Inc., USA.

Digital Library

[13]

Raymond Lister, Elizabeth S. Adams, Sue Fitzgerald, William Fone, John Hamer, Morten Lindholm, Robert McCartney, Jan Erik Moström, Kate Sanders, Otto Seppälä, Beth Simon, and Lynda Thomas. 2004. A Multi-National Study of Reading and Tracing Skills in Novice Programmers. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (Leeds, United Kingdom) (ITiCSE-WGR ’04). Association for Computing Machinery, New York, NY, USA, 119–150. https://doi.org/10.1145/1044550.1041673

Digital Library

[14]

Linxiao Ma, John Ferguson, Marc Roper, and Murray Wood. 2007. Investigating the Viability of Mental Models Held by Novice Programmers. In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education (Covington, Kentucky, USA) (SIGCSE ’07). ACM, New York, NY, USA, 499–503. https://doi.org/10.1145/1227310.1227481

Digital Library

[15]

Michael McCracken, Vicki Almstrum, Danny Diaz, Mark Guzdial, Dianne Hagan, Yifat Ben-David Kolikant, Cary Laxer, Lynda Thomas, Ian Utting, and Tadeusz Wilusz. 2001. A Multi-National, Multi-Institutional Study of Assessment of Programming Skills of First-Year CS Students. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (Canterbury, UK) (ITiCSE-WGR ’01). Association for Computing Machinery, New York, NY, USA, 125–180. https://doi.org/10.1145/572133.572137

Digital Library

[16]

J. A. Nelder and R. W. M. Wedderburn. 1972. Generalized Linear Models. Journal of the Royal Statistical Society. Series A (General) 135, 3(1972), 370–384. https://doi.org/10.2307/2344614

[17]

Greg L. Nelson, Filip Strömbäck, Ari Korhonen, Marjahan Begum, Ben Blamey, Karen H. Jin, Violetta Lonati, Bonnie MacKellar, and Mattia Monga. 2020. Differentiated Assessments for Advanced Courses That Reveal Issues with Prerequisite Skills: A Design Investigation. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (Trondheim, Norway) (ITiCSE-WGR ’20). Association for Computing Machinery, New York, NY, USA, 75–129. https://doi.org/10.1145/3437800.3439204

Digital Library

[18]

Jacob Perrenet and Eric Kaasenbrood. 2006. Levels of Abstraction in Students’ Understanding of the Concept of Algorithm: The Qualitative Perspective. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (Bologna, Italy) (ITICSE ’06). Association for Computing Machinery, New York, NY, USA, 270–274. https://doi.org/10.1145/1140124.1140196

Digital Library

[19]

Yizhou Qian and James Lehman. 2017. Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review. ACM Trans. Comput. Educ. 18, 1, Article 1 (oct 2017), 24 pages. https://doi.org/10.1145/3077618

Digital Library

[20]

Yolanda J. Reimer, Michael Coe, Lisa M. Blank, and Jeffrey Braun. 2018. Effects of Professional Development on Programming Knowledge and Self-Efficacy. In 2018 IEEE Frontiers in Education Conference (FIE). 1–8. https://doi.org/10.1109/FIE.2018.8659041

Digital Library

[21]

Teemu Sirkiä and Juha Sorva. 2012. Exploring Programming Misconceptions: An Analysis of Student Mistakes in Visual Program Simulation Exercises. 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, 19–28. https://doi.org/10.1145/2401796.2401799

Digital Library

[22]

David Statter and Michal Armoni. 2020. Teaching Abstraction in Computer Science to 7th Grade Students. ACM Trans. Comput. Educ. 20, 1, Article 8 (jan 2020), 37 pages. https://doi.org/10.1145/3372143

Digital Library

[23]

Filip Strömbäck, Linda Mannila, Mikael Asplund, and Mariam Kamkar. 2019. A Student’s View of Concurrency - A Study of Common Mistakes in Introductory Courses on Concurrency. In Proceedings of the 2019 ACM Conference on International Computing Education Research (Toronto ON, Canada) (ICER ’19). Association for Computing Machinery, New York, NY, USA, 229–237. https://doi.org/10.1145/3291279.3339415

Digital Library

[24]

Filip Strömbäck, Linda Mannila, and Mariam Kamkar. 2021. The Non-Deterministic Path to Concurrency – Exploring how Students Understand the Abstractions of Concurrency. Informatics in Education 20, 4 (2021), 683–715. https://doi.org/10.15388/infedu.2021.29

Cited By

Lu KKrishnamurthi S(2024)Identifying and Correcting Programming Language Behavior MisconceptionsProceedings of the ACM on Programming Languages10.1145/36498238:OOPSLA1(334-361)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649823
Haglund PMannila LStrömbäck FBerglund AMonga MLonati VBarendsen ESheard JPaterson J(2024)Grasping the Unseen: TA Insights into Teaching Subtle Concepts in Computer ScienceProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653601(157-163)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653601

Index Terms

The Progression of Students’ Ability to Work With Scope, Parameter Passing and Aliasing
1. Applied computing
  1. Education
2. General and reference
  1. Cross-computing tools and techniques
    1. Empirical studies

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cover image ACM Other conferences

ACE '23: Proceedings of the 25th Australasian Computing Education Conference

January 2023

139 pages

ISBN:9781450399418

DOI:10.1145/3576123

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 January 2023

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ACE '23

ACE '23: Australasian Computing Education Conference

January 30 - February 3, 2023

VIC, Melbourne, Australia

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Cited By

Lu KKrishnamurthi S(2024)Identifying and Correcting Programming Language Behavior MisconceptionsProceedings of the ACM on Programming Languages10.1145/36498238:OOPSLA1(334-361)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649823
Haglund PMannila LStrömbäck FBerglund AMonga MLonati VBarendsen ESheard JPaterson J(2024)Grasping the Unseen: TA Insights into Teaching Subtle Concepts in Computer ScienceProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653601(157-163)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653601

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