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An automatic grading scheme for simple programming exercises

Authors:

J. B. Hext,

J. W. WiningsAuthors Info & Claims

Communications of the ACM, Volume 12, Issue 5

Pages 272 - 275

https://doi.org/10.1145/362946.362981

Published: 01 May 1969 Publication History

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Abstract

A discussion is given of alterations that were made to a typical university operating system to record the results of programming exercises in three different languages, includeing assembly language. In this computer-controlled grading scheme provision is made for testing with programmer-supplied data and for final runs with system-supplied data. Exercises run under the scheme may be mixed with other programs, and no special recognition of exercises by the operators is necessary.

References

[1]

HOLLINOSWORTH, J. Automatic graders for programming classes. Comm. ACM 3, 10 (Oct. 1960), 528-529.

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[2]

NAUR, P. Automatic grading of students' ALGOL programming. BIT 4 (1964), 177-188.

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[3]

FORSYTHE, G. E. AND WIRTH, N. Automatic grading programs. Comm. ACM 8, 5 (May 1965), 275-278.

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[4]

TEMPERLY, J. F. AND SMITH, B.W. A grading procedure for PL/1 student exercises. Comput. J . 10 (Feb. 1968), 368-370.

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Brieven GBaum VDonnet B(2024)Tartare: Automatic Generation of C Pointer Statements and FeedbackProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636264(192-201)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636264
Lagakis PDemetriadis SPsathas G(2024)Automated Grading in Coding Exercises Using Large Language ModelsSmart Mobile Communication & Artificial Intelligence10.1007/978-3-031-54327-2_37(363-373)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-3-031-54327-2_37
Fan ZTan SRoychoudhury AJust RFraser G(2023)Concept-Based Automated Grading of CS-1 Programming AssignmentsProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598049(199-210)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598049
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Index Terms

An automatic grading scheme for simple programming exercises
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Model curricula
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems

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Published In

Communications of the ACM Volume 12, Issue 5

May 1969

95 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/362946

Editor:
M. Stuart Lynn
IBM Research Center, Houston, TX

Issue’s Table of Contents

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1969

Published in CACM Volume 12, Issue 5

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Brieven GBaum VDonnet B(2024)Tartare: Automatic Generation of C Pointer Statements and FeedbackProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636264(192-201)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636264
Lagakis PDemetriadis SPsathas G(2024)Automated Grading in Coding Exercises Using Large Language ModelsSmart Mobile Communication & Artificial Intelligence10.1007/978-3-031-54327-2_37(363-373)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-3-031-54327-2_37
Fan ZTan SRoychoudhury AJust RFraser G(2023)Concept-Based Automated Grading of CS-1 Programming AssignmentsProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598049(199-210)Online publication date: 12-Jul-2023
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Rico-Juan JSánchez-Cartagena VValero-Mas JGallego A(2023)Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial IntelligenceIEEE Transactions on Learning Technologies10.1109/TLT.2023.323911016:6(955-969)Online publication date: 1-Dec-2023
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Paiva JLeal JFigueira Á(2022)Automated Assessment in Computer Science Education: A State-of-the-Art ReviewACM Transactions on Computing Education10.1145/351314022:3(1-40)Online publication date: 9-Jun-2022
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Iffath FKayes ARahman MFerdows JArefin MHossain M(2021)Online Judging Platform Utilizing Dynamic Plagiarism Detection FacilitiesComputers10.3390/computers1004004710:4(47)Online publication date: 8-Apr-2021
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Charitsis CPiech CMitchell J(2021)Simplifying Automated Assessment in CS1Proceedings of the 2021 4th International Conference on Education Technology Management10.1145/3510309.3510345(226-231)Online publication date: 17-Dec-2021
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Xu BYan SJiang XFeng S(2020)SCFH: A Student Analysis Model to Identify Students’ Programming Levels in Online Judge SystemsSymmetry10.3390/sym1204060112:4(601)Online publication date: 10-Apr-2020
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Leite ABlanco SZhang JSherriff MHeckman SCutter PMonge A(2020)Effects of Human vs. Automatic Feedback on Students' Understanding of AI Concepts and Programming StyleProceedings of the 51st ACM Technical Symposium on Computer Science Education10.1145/3328778.3366921(44-50)Online publication date: 26-Feb-2020
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Liu XWang SWang PWu DBeecham SDamian D(2019)Automatic grading of programming assignmentsProceedings of the 41st International Conference on Software Engineering: Software Engineering Education and Training10.1109/ICSE-SEET.2019.00022(126-137)Online publication date: 27-May-2019
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Evaluating Automatically Generated Contextualised Programming Exercises

Getting more oomph from programming exercises

Getting more oomph from programming exercises

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