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A multi-institutional study of peer instruction in introductory computing

Authors:

Dennis Bouvier,

Robert McCartney,

Daniel Zingaro,

Beth SimonAuthors Info & Claims

ACM Inroads, Volume 7, Issue 2

Pages 76 - 81

https://doi.org/10.1145/2938142

Published: 16 May 2016 Publication History

Abstract

Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. Over the past five years, there have been a number of research articles regarding the value of PI in computer science. The present work adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, we provide evidence that introductory computing instructors can successfully implement PI in their classrooms. We find encouraging minimum (74%) and average (92%) levels of success as measured through student valuation of PI for their learning. This work also documents and hypothesizes reasons for comparatively poor survey results in one course, highlighting the importance of the choice of grading policy (participation vs. correctness) for new PI adopters.

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Cited By

Sankaran SSankaran KBui T(2023)Student satisfaction with R vs. Excel in Data Mining and Business Analytics: A Herzberg's motivation‐hygiene theory perspectiveDecision Sciences Journal of Innovative Education10.1111/dsji.1228521:2(68-82)Online publication date: 8-Feb-2023
https://doi.org/10.1111/dsji.12285
Benndorf MKind GSiewerts HHeinke FSchneider U(2020)Towards a better understanding of students in the entry phase of their studiesProceedings of the 9th Computer Science Education Research Conference10.1145/3442481.3442499(1-7)Online publication date: 19-Oct-2020
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Burgiel HSadler PSonnert G(2020)The Association of High School Computer Science Content and Pedagogy with Students’ Success in College Computer ScienceACM Transactions on Computing Education10.1145/338199520:2(1-21)Online publication date: 24-Apr-2020
https://dl.acm.org/doi/10.1145/3381995
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A multi-institutional study of peer instruction in introductory computing
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs

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Published In

cover image ACM Inroads

ACM Inroads Volume 7, Issue 2

June 2016

76 pages

ISSN:2153-2184

EISSN:2153-2192

DOI:10.1145/2938622

Editors:
Mark Bailey
Professor of Computer Science, Hamilton College
,
Laurie Smith King
Professor of Computer Science, College of the Holy Cross

Issue’s Table of Contents

Copyright © 2016 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 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

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Publication History

Published: 16 May 2016

Published in INROADS Volume 7, Issue 2

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Cited By

Sankaran SSankaran KBui T(2023)Student satisfaction with R vs. Excel in Data Mining and Business Analytics: A Herzberg's motivation‐hygiene theory perspectiveDecision Sciences Journal of Innovative Education10.1111/dsji.1228521:2(68-82)Online publication date: 8-Feb-2023
https://doi.org/10.1111/dsji.12285
Benndorf MKind GSiewerts HHeinke FSchneider U(2020)Towards a better understanding of students in the entry phase of their studiesProceedings of the 9th Computer Science Education Research Conference10.1145/3442481.3442499(1-7)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3442481.3442499
Burgiel HSadler PSonnert G(2020)The Association of High School Computer Science Content and Pedagogy with Students’ Success in College Computer ScienceACM Transactions on Computing Education10.1145/338199520:2(1-21)Online publication date: 24-Apr-2020
https://dl.acm.org/doi/10.1145/3381995
Silva DAguiar RDvconlo DSilla C(2020)Recent Studies About Teaching Algorithms (CS1) and Data Structures (CS2) for Computer Science Students2019 IEEE Frontiers in Education Conference (FIE)10.1109/FIE43999.2019.9028702(1-8)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1109/FIE43999.2019.9028702
Kandemir CKalelioğlu FGülbahar Y(2020)Pedagogy of teaching introductory text‐based programming in terms of computational thinking concepts and practicesComputer Applications in Engineering Education10.1002/cae.2237429:1(29-45)Online publication date: 10-Dec-2020
https://doi.org/10.1002/cae.22374
Bouvier DLovellette EMatta JBai JChetty JKurkovsky SWan JSheard J(2019)Factors Affecting the Adoption of Peer Instruction in Computing CoursesProceedings of the Working Group Reports on Global Computing Education10.1145/3372262.3375396(1-25)Online publication date: 18-Dec-2019
https://dl.acm.org/doi/10.1145/3372262.3375396
Izu CSchulte CAggarwal ACutts QDuran RGutica MHeinemann BKraemer ELonati VMirolo CWeeda RRößling GGiannakos MScharlau BMcDermott RPears ASabin M(2019)Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning TrajectoriesProceedings of the Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3344429.3372501(27-52)Online publication date: 18-Dec-2019
https://dl.acm.org/doi/10.1145/3344429.3372501
Nelson GKo A(2019)John Henry AWARDACM Inroads10.1145/330615310:1(74-83)Online publication date: 8-Feb-2019
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Liao SValstar SThai KAlvarado CZingaro DGriswold WPorter LScharlau BMcDermott RPears ASabin M(2019)Behaviors of Higher and Lower Performing Students in CS1Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3304221.3319740(196-202)Online publication date: 2-Jul-2019
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Liao SZingaro DThai KAlvarado CGriswold WPorter L(2019)A Robust Machine Learning Technique to Predict Low-performing StudentsACM Transactions on Computing Education10.1145/327756919:3(1-19)Online publication date: 16-Jan-2019
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