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An Analysis of Using Many Small Programs in CS1

Published: 22 February 2019 Publication History

Abstract

Modern program auto-graders enable new CS1 approaches. Instructors can easily create new assignments, with students receiving immediate score feedback and resubmitting assignments. With such auto-graders, one approach assigns many small programs (MSPs) each week instead of one large program (OLP). Earlier research showed MSPs in CS1 yielded happier students and better grades. Our university and other schools have switched to MSPs in CS1. This paper addresses common questions about MSPs. We analyzed submissions for a 76-student section of our MSP CS1 course. Given 7 MSPs per week each worth 10 points, students needed 50 points for full credit. Students averaged 17 minutes per MSP and 120 minutes per week. Given 7 days, students on average started 2.2 days ahead of the due date, with 37% starting at least 3 days ahead. 40% of students exceeded the required 50 points per week (no extra credit was given). 50% of students "pivoted" -- switching to another program before completing the previous one. 54% used MSPs to study for exams. Students used MSPs in ways beneficial to their learning and stress reduction: spending sufficient time, completing more than necessary, preparing for exams, and pivoting to avoid getting stuck. A common concern is that MSP CS1 students will do poorly in a CS2 using OLPs. We analyzed 5 quarters of CS2 and found MSP students do fine (in fact slightly better). These results encourage use and refinement of MSPs in CS1 and other courses.

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cover image ACM Conferences
SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education
February 2019
1364 pages
ISBN:9781450358903
DOI:10.1145/3287324
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].

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Publication History

Published: 22 February 2019

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Author Tags

  1. auto-grader
  2. cs1
  3. cs2
  4. days before due
  5. exam preparation
  6. msps
  7. pivot
  8. programming
  9. threshold
  10. time spent

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  • Research-article

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  • U.S. Dept. of Education (GAANN fellowship)
  • Google

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SIGCSE '19
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SIGCSE '19 Paper Acceptance Rate 169 of 526 submissions, 32%;
Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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Cited By

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  • (2024)CS1-LLM: Integrating LLMs into CS1 InstructionProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653584(297-303)Online publication date: 3-Jul-2024
  • (2024)A Literature-Informed Model for Code Style Principles to Support Teachers of Text-Based ProgrammingProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636258(134-143)Online publication date: 29-Jan-2024
  • (2024)The Widening Gap: The Benefits and Harms of Generative AI for Novice ProgrammersProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671116(469-486)Online publication date: 12-Aug-2024
  • (2023)The Applications of Learning Analytics to Enhance Learning and Engagement in Introductory Programming InstructionPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch005(89-108)Online publication date: 24-Oct-2023
  • (2023)The Robots Are Here: Navigating the Generative AI Revolution in Computing EducationProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633499(108-159)Online publication date: 22-Dec-2023
  • (2023)A Think-Aloud Study of Novice DebuggingACM Transactions on Computing Education10.1145/358900423:2(1-38)Online publication date: 8-Jun-2023
  • (2023)A Practical Strategy for Training Graduate CS Teaching Assistants to Provide Effective FeedbackProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588776(285-291)Online publication date: 29-Jun-2023
  • (2023)Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online JudgesIEEE Access10.1109/ACCESS.2023.324718911(22513-22525)Online publication date: 2023
  • (2022)MOCSIDEJournal of Computing Sciences in Colleges10.5555/3512733.351273437:5(11-20)Online publication date: 19-Jan-2022
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