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Mining autograding data in computer science education

Published: 01 February 2016 Publication History

Abstract

In this paper we present an analysis of the impact of instant feedback and autograding in computer science education, beyond the classic Introduction to Programming subject.
We analysed the behaviour of 1st year to 4th year students when submitting programming assignments at the University of Sydney over a period of 3 years. These assignments were written in different programming languages, such as C, C++, Java and Python, for diverse computer science courses, from fundamental ones---algorithms, complexity, formal languages, data structures and artificial intelligence to more "practical" ones---programming, distributed systems, databases and networks.
We observed that instant feedback and autograding can help students and instructors in subjects not necessarily focused on programming. We also discuss the relationship between the student performance in these subjects and the choice of programming languages or the times at which a student starts and stops working on an assignment.

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Cited By

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  • (2024)Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature ReviewProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630958(172-178)Online publication date: 7-Mar-2024

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Published In

cover image ACM Other conferences
ACSW '16: Proceedings of the Australasian Computer Science Week Multiconference
February 2016
654 pages
ISBN:9781450340427
DOI:10.1145/2843043
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 February 2016

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Author Tags

  1. educational data mining
  2. instant feedback
  3. learning analytics

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  • Research-article

Funding Sources

  • Australian Government through the Department of Communications and the Australian Research Council

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ACSW '16
ACSW '16: Australasian Computer Science Week
February 1 - 5, 2016
Canberra, Australia

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ACSW '16 Paper Acceptance Rate 77 of 172 submissions, 45%;
Overall Acceptance Rate 204 of 424 submissions, 48%

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View all
  • (2024)Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature ReviewProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630958(172-178)Online publication date: 7-Mar-2024

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