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Identifying and Validating Java Misconceptions Toward a CS1 Concept Inventory

Authors:

Ricardo Caceffo,

Pablo Frank-Bolton,

Rodolfo AzevedoAuthors Info & Claims

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

Pages 23 - 29

https://doi.org/10.1145/3304221.3319771

Published: 02 July 2019 Publication History

Abstract

A misconception is a common misunderstanding that students may have about a specific topic. The identification, documentation, and validation of misconceptions is a long and time-consuming work, usually carried out using iterative cycles of students answering open-ended questionnaires, interviews with instructors and students, exam analysis, and discussion with experts. A comprehensive list of validated misconceptions in some subject can be used to build formal evaluation methods like the Concept Inventory (CI), a multiple-choice questionnaire that is usually performed as pre-post tests in order to assess any change in student understanding. In CS1, validated misconceptions were identified and documented in C and Python programming languages. Although there are studies related to misconceptions in the Java language, these misconceptions lack the formality, comprehensiveness, and robustness of their C and Python counterparts. On this work, we propose a methodology to adapt the validated misconceptions in C and Python to Java. Initially, through the analysis of an initial list of 33 misconceptions in C and 28 in Python, we identified and documented in an antipattern format 31 possible misconceptions in Java. We then developed a final term exam, composed of 7 open-ended questions, in which each question was designed to address some of the misconceptions covered in the course (N=27). Through the analysis of the exams answers (N = 69 students), it was possible to validate 22 of the misconceptions (81%). Also, 6 new misconceptions were identified, leading to a total of 28 valid misconceptions in Java.

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Cited By

Vieira CVásquez AMeza FQuintero-Manes RGodoy P(2024)Identifying Difficult Questions and Student Difficulties in a Spanish Version of a Programming Assessment Instrument (SCS1)ACM Transactions on Computing Education10.1145/366592124:3(1-17)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665921
Ali MRao PMai YXie B(2024)Using Benchmarking Infrastructure to Evaluate LLM Performance on CS Concept Inventories: Challenges, Opportunities, and CritiquesProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671097(452-468)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671097
Netere AHughes TBabey AHawes MMifsud JKelly JLiang WHernandez MKarpa KAl‐Sallami HFernandes LAronsson PRestini CCaetano Crowley FDjouma EHinton TLiu JMraiche FWhite P(2024)Evaluating the quality of multiple‐choice question pilot database: A global educator‐created tool for concept‐based pharmacology learningPharmacology Research & Perspectives10.1002/prp2.7000412:5Online publication date: 13-Sep-2024
https://doi.org/10.1002/prp2.70004
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Index Terms

Identifying and Validating Java Misconceptions Toward a CS1 Concept Inventory
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        CS1
      2. Student assessment
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Patterns

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cover image ACM Conferences

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

July 2019

583 pages

ISBN:9781450368957

DOI:10.1145/3304221

General Chairs:
Bruce Scharlau
University of Aberdeen, UK
,
Roger McDermott
Robert Gordon University, UK
,
Program Chairs:
Arnold Pears
KTH Royal Institute of Technology, Sweden
,
Mihaela Sabin
University of New Hampshire, USA

Copyright © 2019 ACM.

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Published: 02 July 2019

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CNPq
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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ITiCSE '19: Innovation and Technology in Computer Science Education

July 15 - 17, 2019

Aberdeen, Scotland Uk

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

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Cited By

Vieira CVásquez AMeza FQuintero-Manes RGodoy P(2024)Identifying Difficult Questions and Student Difficulties in a Spanish Version of a Programming Assessment Instrument (SCS1)ACM Transactions on Computing Education10.1145/366592124:3(1-17)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665921
Ali MRao PMai YXie B(2024)Using Benchmarking Infrastructure to Evaluate LLM Performance on CS Concept Inventories: Challenges, Opportunities, and CritiquesProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671097(452-468)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671097
Netere AHughes TBabey AHawes MMifsud JKelly JLiang WHernandez MKarpa KAl‐Sallami HFernandes LAronsson PRestini CCaetano Crowley FDjouma EHinton TLiu JMraiche FWhite P(2024)Evaluating the quality of multiple‐choice question pilot database: A global educator‐created tool for concept‐based pharmacology learningPharmacology Research & Perspectives10.1002/prp2.7000412:5Online publication date: 13-Sep-2024
https://doi.org/10.1002/prp2.70004
Zimmermann RAllin SZhang L(2023)Common Errors in Machine Learning Projects: A Second LookProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631808(1-12)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3631802.3631808
Whalley JSettle ALuxton-Reilly A(2023)A Think-Aloud Study of Novice DebuggingACM Transactions on Computing Education10.1145/358900423:2(1-38)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1145/3589004
Evans AWang ZLiu JZheng MLaakso MMonga MSimon Sheard J(2023)SIDE-lib: A Library for Detecting Symptoms of Python Programming MisconceptionsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588838(159-165)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588838
Ali MGhosh SRao PDhegaskar RJawort SMedler AShi MDasgupta S(2023)Taking Stock of Concept Inventories in Computing Education: A Systematic Literature ReviewProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600120(397-415)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600120
Esche SWeihe K(2023)Case Study on the Terms Novice Programmers Use to Describe Code Snippets in JavaIEEE Transactions on Education10.1109/TE.2023.329025966:6(642-653)Online publication date: Dec-2023
https://doi.org/10.1109/TE.2023.3290259
Mazumder SPérez-Quiñones M(2023)Incoming CS1 Students' Misconceptions on Arrays2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342927(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10342927
Adamoli A(2023)An Agile Concept Inventory Methodology to Detect Large Sets of Student Misconceptions in Programming Language CoursesResponsive and Sustainable Educational Futures10.1007/978-3-031-42682-7_1(1-15)Online publication date: 28-Aug-2023
https://doi.org/10.1007/978-3-031-42682-7_1
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