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On understanding compatibility of student pair programmers

Authors:

Ed GehringerAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 36, Issue 1

Pages 7 - 11

https://doi.org/10.1145/1028174.971307

Published: 01 March 2004 Publication History

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Abstract

In recent years, educators have increasingly used pair programming in their computer science courses. Pair programming has been shown to be beneficial for both the teaching staff and the students in the courses. Occasionally, though, students are not compatible with their partners. An extensive study was done at the North Carolina State University to provide guidance on forming student pairs to improve the chances that pairs will be compatible and have a productive work relationship. We examined compatibility among freshman, advanced undergraduate, and graduate students. We have found that the students' perception of their partner's skill level has a significant influence on their compatibility. Graduate students work well with partners of similar actual skill level. Freshmen seem to work better with partners with different Myers Briggs personality type. Students' self-esteem does not appear to be a major contributor to pair compatibility.

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Index Terms

On understanding compatibility of student pair programmers
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
      1. Programming teams

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Information

Published In

ACM SIGCSE Bulletin Volume 36, Issue 1

March 2004

501 pages

ISSN:0097-8418

DOI:10.1145/1028174

Issue’s Table of Contents

SIGCSE '04: Proceedings of the 35th SIGCSE technical symposium on Computer science education
March 2004
544 pages
ISBN:1581137982
DOI:10.1145/971300
Conference Chairs:
Dan Joyce
Villanova University
,
Deborah Knox
The College of New Jersey
,
Program Chairs:
Wanda Dann
Ithaca College
,
Tom Naps
University of Wisconsin - Oshkosh

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2004

Published in SIGCSE Volume 36, Issue 1

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Cited By

View all

Mai TBezbradica MCrane M(2023)Computerunterstütztes kollaboratives Lernen: Neueste Erkenntnisse über Tools zur Unterstützung der Lerndynamik in GruppenEducational Data Mining und Learning Analytics10.1007/978-3-658-39607-7_2(41-83)Online publication date: 10-Jun-2023
https://doi.org/10.1007/978-3-658-39607-7_2
Robe PKuttal S(2022)Designing PairBuddy—A Conversational Agent for Pair ProgrammingACM Transactions on Computer-Human Interaction10.1145/349832629:4(1-44)Online publication date: 5-May-2022
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Hart JAuBuchon JKuttal S(2022)Feasibility of using YouTube Conversations for Pair Programming Intent Classification2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC53370.2022.9833139(1-4)Online publication date: 12-Sep-2022
https://doi.org/10.1109/VL/HCC53370.2022.9833139
Kudrowitz BDavel R(2022)First Impressions Matter (Maybe More Than Prior Design Experience): The Predictive Power of Soft-Skills on Student Performance in a Team-Based Design Course[ ] With Design: Reinventing Design Modes10.1007/978-981-19-4472-7_23(339-356)Online publication date: 6-Nov-2022
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