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Micro-Classes: A Structure for Improving Student Experience in Large Classes

Authors:

Christine Alvarado,

Leo PorterAuthors Info & Claims

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

Pages 21 - 26

https://doi.org/10.1145/3017680.3017727

Published: 08 March 2017 Publication History

Abstract

As class-sizes grow in computer science, the personal attention received by students tends to diminish. This work aims to replicate small-class community effects within a large class by creating "micro-classes"---small groups within the large class. These micro-classes consist of 20--30 students led by graduate teaching assistants and undergraduate tutors who are specifically trained in small-classroom instructional techniques. This paper studies the outcomes of the micro-classes framework in an upper-division data structures course and compares them to outcomes from the same class taught in a large lecture, active-learning format. Students report increased satisfaction and a higher perception of community in the micro-classes section, though there was no discernible difference in student academic performance.

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Cited By

Perlmutter LSalac JKo A(2023)"A field where you will be accepted": Belonging in student and TA interactions in post-secondary CS educationProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600128(356-370)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600128
Hazzan ORagonis NLapidot THazzan ORagonis NLapidot T(2020)Teaching Methods in Computer Science EducationGuide to Teaching Computer Science10.1007/978-3-030-39360-1_10(181-220)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-39360-1_10

Index Terms

Micro-Classes: A Structure for Improving Student Experience in Large Classes
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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Published In

cover image ACM Conferences

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

March 2017

838 pages

ISBN:9781450346986

DOI:10.1145/3017680

General Chairs:
Michael E. Caspersen
Aarhus University
,
Stephen H. Edwards
Virginia Tech
,
Program Chairs:
Tiffany Barnes
North Carolina State University
,
Daniel D. Garcia
University of California, Berkeley

Copyright © 2017 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].

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Published: 08 March 2017

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SIGCSE '17: The 48th ACM Technical Symposium on Computer Science Education

March 8 - 11, 2017

Washington, Seattle, USA

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SIGCSE '17 Paper Acceptance Rate 105 of 348 submissions, 30%;

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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Cited By

Perlmutter LSalac JKo A(2023)"A field where you will be accepted": Belonging in student and TA interactions in post-secondary CS educationProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600128(356-370)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568813.3600128
Hazzan ORagonis NLapidot THazzan ORagonis NLapidot T(2020)Teaching Methods in Computer Science EducationGuide to Teaching Computer Science10.1007/978-3-030-39360-1_10(181-220)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-39360-1_10

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