research-article

SIDE-lib: A Library for Detecting Symptoms of Python Programming Misconceptions

Authors:

Mingming ZhengAuthors Info & Claims

ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1

Pages 159 - 165

https://doi.org/10.1145/3587102.3588838

Published: 30 June 2023 Publication History

Abstract

Extensive prior work has identified and described misconceptions held by novice programmers. Much of this prior work has involved at least some automatic detection of potential misconceptions using a variety of methods such as intercepting compiler error messages, pattern matching, and black-box testing. To the best of our knowledge, no independent and flexible tool for automatic detection of misconceptions is currently available to the research community, meaning that detection must be reimplemented from scratch for each new project that aims to understand or support novice programmers using automatic analysis. This is time-consuming work, particularly for misconceptions that require understanding of the context of a program beyond localised syntax patterns. In this paper, we introduce SIDE-lib, a standalone library for detecting symptoms of Python misconceptions. This library is made available with the goal of simplifying and speeding up research on Python misconceptions and the development of tools to support learning. We also describe example use cases for the library, including how we are using it in our ongoing research.

References

[1]

Ella Albrecht and Jens Grabowski. 2020. Sometimes It's Just Sloppiness - Studying Students' Programming Errors and Misconceptions. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE '20). ACM, Portland OR USA, 340--345. https://doi.org/10.1145/3328778.3366862

Digital Library

[2]

Amjad Altadmri and Neil C. C. Brown. 2015. 37 Million Compilations: Investigating Novice Programming Mistakes in Large Scale Student Data. In Proceedings of SIDE-lib: A Library for Detecting Symptoms of Python Programming Misconceptions ITiCSE 2023, July 8--12, 2023, Turku, Finland. the 45th ACM Technical Symposium on Computer Science Education (SIGCSE '15). ACM, Kansas City MO USA, 522--527. https://doi.org/10.1145/2676723.2677258

Digital Library

[3]

Piraye Bayman and Richard E. Mayer. 1983. A Diagnosis of Beginning Programmers' Misconceptions of BASIC Programming Statements. Commun. ACM 54, 9 (Sept. 1983), 677--679. https://doi.org/10.1145/358172.358408

Digital Library

[4]

Piraya Bayman and Richard E. Mayer. 1988. Using Conceptual Models to Teach BASIC Computer Programming. Journal of Educational Psychology 80, 3 (1988), 291--298.

[5]

Neil C. C. Brown and Amad Altadmri. 2014. Investigating Novice Programming Mistakes: Educator Beliefs vs. Student Data. In Proceedings of the 10th Annual Conference on International Computing Education Research (ICER '14).

[6]

Neil C. C. Brown, Michael Kölling, Davin McCall, and Ian Utting. 2014. Blackbox: A Large Scale Repository of Novice Programmers' Activity. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE '14). ACM, Atlanta GA USA, 223--228. https://doi.org/10.1145/2538862.2538924

Digital Library

[7]

Ricardo Caceffo, Pablo Frank-Bolton, Renan Souza, and Rodolfo Azevedo. 2019. Identifying and Validating Java Misconceptions Toward a CS1 Concept Inventory. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '19). ACM, Aberdeen Scotland UK, 23--29. https://doi.org/10.1145/3304221.3319771

Digital Library

[8]

Luca Chiodini, Igor Moreno Santos, Andrea Gallidabino, Anya Tafliovich, André L. Santos, and Matthias Hauswirth. 2021. A Curated Inventory of Programming Language Misconceptions. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '21). ACM, Virtual Event, 380--386. https://doi.org/10.1145/3430665.3456343

Digital Library

[9]

Thomas Dy and Ma Mercedes Rodrigo. 2010. A Detector for Non-Literal Java Errors. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research (Koli Calling, '10). ACM, Berlin, Germany, 118--122. https://doi.org/10.1145/1930464.1930485

Digital Library

[10]

Anna Eckerdal and Michael Thune. 2005. Novice Java programmers' conceptions of 'object' and 'class', and variation theory. In Proceedings of the 2005 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '05). ACM, Monte de Caparica Portugal. https://doi.org/10.1145/1067445.1067473

Digital Library

[11]

Andrew Ettles, Andrew Luxton-Reilly, and Paul Denny. 2018. Common Logic Errors Made By Novice Programmers. In Proceedings of the 20th Australasian Computing Education Conference (ACE '18). ACM, Brisbane Australia, 83--89. https://doi.org/10.1145/3160489.3160493

Digital Library

[12]

Ann E. Fleury. 1991. Parameter Passing: The Rules Students Construct. In Proceedings of the Twenty-Second SIGCSE Technical Symposium on Computer Science Education (SIGCSE '91). ACM, San Antonio TX USA, 283--286. https: //doi.org/10.1145/107004.107066

Digital Library

[13]

Ann E. Fleury. 2000. Programming in Java: Student-Constructed Rules. In Proceedings of the Thirty-First SIGCSE Technical Symposium on Computer Science Education (SIGCSE '00). ACM, Austin TX USA, 197--201. https://doi.org/10.1145/ 330908.331854

Digital Library

[14]

Python Software Foundation. 2023. Execution Model. Retrieved Jan 17, 2023 from https://docs.python.org/3/referenceexecutionmodel.html

[15]

Guilherme Gama, Ricardo Caceffo, Renan Souza, Raysa Benatti, Tales Aparecida, Islene Garcia, and Rodolfo Azevedo. 2018. An Antipattern Documentation about Misconceptions related to an Introductory Programming Course in Python. Technical Report IC-18--19. Institute of Computing, University of Campinas, Campinas Brazil.

[16]

Luke Gusukuma, Austin Cory Bart, and Dennis Kafura. 2020. Pedal: An Infrastructure for Automated Feedback Systems. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE '20). ACM, Portland OR USA, 1061--1067. https://doi.org/10.1145/3328778.3366913

Digital Library

[17]

Luke Gusukuma, Austin Cory Bart, Dennis Kafura, and Jeremy Ernst. 2018. Misconception-Driven Feedback: Results from an Experimental Study. In Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER '18). ACM, Espoo Finland, 160--168. https://doi.org/10.1145/3230977. 3231002

Digital Library

[18]

Simon Holland, Robert Griffiths, and Mark Woodman. 1997. Avoiding Object Misconceptions. In Proceedings of the 28th ACM Technical Symposium on Computer Science Education (SIGCSE '97). ACM, CA USA, 131--134. https://doi.org/10.1145/ 268084.268132

Digital Library

[19]

Maria Hristova, Ananya Misra, Megan Rutter, and Rebecca Mercuri. 2003. Identifying and Correcting Java Programming Errors for Introductory Computer Science Students. In Proceedings of the 34th ACM Technical Symposium on Computer Science Education (SIGCSE '03). ACM, Reno NV USA, 153--156. https://doi.org/10.1145/611892.611956

Digital Library

[20]

Matthew C Jadud. 2005. A First Look at Novice Compilation Behaviour Using BlueJ. Computer Science Education 15, 1 (2005), 25--40. https://doi.org/10.1080/ 08993400500056530

[21]

Fionnuala Johnson, Stephen McQuistin, and John O'Donnell. 2020. Analysis of Student Misconceptions using Python as an Introductory Programming Language. In Proceedings of the 4th Conference on Computing Education Practice 2020 (CEP 2020). ACM, Durham UK, 1--4. https://doi.org/10.1145/3372356.3372360

Digital Library

[22]

Fionnuala Johnson, Stephen McQuistin, John O'Donnell, and Quintin Cutts. 2022. Experience Report: Identifying Unexpected Programming Misconceptions with a Computer Systems Approach. In Proceedings of the 2022 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '22). ACM, Dublin Ireland. https://doi.org/10.1145/3502718.3524775

Digital Library

[23]

Lisa Kaczmarczyk, Elizabeth R. Petrick, Philip J. East, and Geoffry L. Herman. 2010. Identifying Student Misconceptions of Programming. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE '10). ACM, Milwaukee WI USA, 107--111. https://doi.org/10.1145/1734263.1734299

Digital Library

[24]

David Liu and Andrew Petersen. 2019. Static Analyses in Python Programming Courses. In Proceedings of the Fiftieth SIGCSE Technical Symposium on Computer Science Education (SIGCSE '19). ACM, Minneapolis MN USA, 666--671. https: //doi.org/10.1145/3287324.3287503

Digital Library

[25]

Davin McCall and Michael Kölling. 2014. Meaningful Categorisation of Novice Programming Errors. In Proceedings of the 2014 Frontiers in Education Conference (FIE '14). Madrid Spain. https://doi.org/10.1109/FIE.2014.7044420

[26]

Davin McCall and Michael Kölling. 2019. A New Look at Novice Programmer Errors. ACM Transactions on Computing Education 9, 4 (2019), 1--30. https: //doi.org/10.1145/3335814

Digital Library

[27]

Tanya J. McGill and Simone E. Volet. 1997. A Conceptual Framework for Analyzing Students' Knowledge of Programming. Journal of Research on Computing in Education 29, 3 (1997), 276--297. https://doi.org/10.1080/08886504.1997.10782199

[28]

Antonija Mitrovic. 2012. Fifteen Years of Constraint-Based Tutors: What We Have Achieved And Where We Are Going. User Modelling and User-Adapted Interaction 1--2 (2012), 39--72. https://doi.org/10.1007/s11257-011--9105--9

[29]

Roy D. Pea. 1986. Language-Independent Conceptual 'Bugs' in Novice Programming. Journal of Educational Computing Research 2, 1 (Feb. 1986). https: //doi.org/10.2190/689T-1R2A-X4W4--29J2

[30]

Yizhou Qian and James Lehman. 2017. Students' Misconceptions and Other Difficulties in Introductory Programming: A Literature Review. ACM Transactions on Computing Education 18, 1 (2017), 24. https://doi.org/10.1145/3077618

Digital Library

[31]

K. Andrew R. Richards and Michael A. Hemphill. 2018. A Practical Guide to Collaborative Qualitative Data Analysis. The Journal of Teaching in Physical Education 37, 2 (2018), 225--231. https://doi.org/10.1123/jtpe.2017-0084

[32]

Kelly Rivers, Erik Harpstead, and Ken Koedinger. 2016. Learning Curve Analysis for Programming: Which Concepts do Students Struggle With?. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER '16). ACM, Melbourne Australia, 143--151. https://doi.org/10.1145/2960310. 2960333

Digital Library

[33]

Kelly Rivers and Kenneth R. Koedinger. 2015. Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Tutor. International Journal of Artificial Intelligence in Education 27, 1 (2015), 37--64. https://doi.org/10.1007/ s40593-015-0070-z

[34]

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 Calling '12). ACM, Koli Finland, 19--28. https://doi.org/10.1145/ 2401796.2401799

Digital Library

[35]

Juha Sorva. 2012. Visual Program Simulation in Introductory Programming Education. Ph.D. Dissertation. Aalto University, Espoo Finland.

Cited By

Cucuiat VWaite JMonga MLonati VBarendsen ESheard JPaterson J(2024)Feedback Literacy: Holistic Analysis of Secondary Educators' Views of LLM Explanations of Program Error MessagesProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653595(192-198)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653595

Index Terms

SIDE-lib: A Library for Detecting Symptoms of Python Programming Misconceptions
1. Applied computing
  1. Education
2. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments

Recommendations

A Curated Inventory of Programming Language Misconceptions
ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1

Knowledge about misconceptions is an important element of pedagogical content knowledge. The computing education research community collected a large body of research on misconceptions, using a diverse set of definitions and approaches. Inspired by this ...
Designing a Supportive IDE to Help Novices Recognise and Recover from Programming Misconceptions
ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2

Popular Integrated Development Environments (IDEs) provide features such as syntax highlighting, warnings, and error messages to help programmers find and fix bugs and write cleaner code. Although these tools are available to programmers of all levels, ...
Identifying student misconceptions of programming
SIGCSE '10: Proceedings of the 41st ACM technical symposium on Computer science education

Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a ...

Comments

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

General Chairs:
Mikko-Jussi Laakso
University of Turku, Finland
,
Mattia Monga
University of Milan, Italy
,
Program Chairs:
Simon
Unaffiliated, Australia
,
Judithe Sheard
Monash University, Australia

Copyright © 2023 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].

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 June 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ITiCSE 2023

Sponsor:

SIGCSE

ITiCSE 2023: Innovation and Technology in Computer Science Education

July 7 - 12, 2023

Turku, Finland

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
112
Total Downloads

Downloads (Last 12 months)58
Downloads (Last 6 weeks)3

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Cucuiat VWaite JMonga MLonati VBarendsen ESheard JPaterson J(2024)Feedback Literacy: Holistic Analysis of Secondary Educators' Views of LLM Explanations of Program Error MessagesProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653595(192-198)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653595

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents