TY - GEN
T1 - A multi-institution exploration of peer instruction in practice
AU - Taylor, Cynthia
AU - Petersen, Andrew
AU - Spacco, Jaime
AU - Bunde, David P.
AU - Liao, Soohyun Nam
AU - Porter, Leo
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Peer Instruction (PI) is an active learning pedagogy that has been shown to improve student outcomes in computing, including lower failure rates, higher exam scores, and better retention in the CS major. PI’s key classroom mechanism is the PI question: a formative multiple choice question on which students vote, then discuss, then vote again. While research indicates that PI questions lead to learning gains for students, relatively little is known about the questions themselves and how faculty employ them. Additionally, much of the work has examined PI data collected by researchers operating in a quasi-experimental setting. We examine data collected incidentally by multiple instructors using PI as a pedagogical technique in their classroom. We look at how many questions instructors use in their courses, the difficulty level of the questions, and normalized gain, a metric that looks at increases in student correctness between individual and group votes. We find normalized gain levels similar to those in existing literature, indicating that students are learning, and that most questions, even those developed by instructors new to PI, fall within recommended difficulty levels, indicating instructors can create good PI questions with little training. We also find that instructors add PI questions over the first several iterations of a new PI course, showing that they find PI questions valuable and suggesting that full development of PI materials for a course may take multiple semesters.
AB - Peer Instruction (PI) is an active learning pedagogy that has been shown to improve student outcomes in computing, including lower failure rates, higher exam scores, and better retention in the CS major. PI’s key classroom mechanism is the PI question: a formative multiple choice question on which students vote, then discuss, then vote again. While research indicates that PI questions lead to learning gains for students, relatively little is known about the questions themselves and how faculty employ them. Additionally, much of the work has examined PI data collected by researchers operating in a quasi-experimental setting. We examine data collected incidentally by multiple instructors using PI as a pedagogical technique in their classroom. We look at how many questions instructors use in their courses, the difficulty level of the questions, and normalized gain, a metric that looks at increases in student correctness between individual and group votes. We find normalized gain levels similar to those in existing literature, indicating that students are learning, and that most questions, even those developed by instructors new to PI, fall within recommended difficulty levels, indicating instructors can create good PI questions with little training. We also find that instructors add PI questions over the first several iterations of a new PI course, showing that they find PI questions valuable and suggesting that full development of PI materials for a course may take multiple semesters.
KW - Active Learning
KW - CS Education
KW - Clicker Questions
KW - Peer Instruction
UR - http://www.scopus.com/inward/record.url?scp=85052027400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052027400&partnerID=8YFLogxK
U2 - 10.1145/3197091.3197144
DO - 10.1145/3197091.3197144
M3 - Conference contribution
AN - SCOPUS:85052027400
T3 - Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE
SP - 308
EP - 313
BT - ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
A2 - Andreou, Panayiotis
A2 - Armoni, Michal
A2 - Read, Janet C.
A2 - Polycarpou, Irene
PB - Association for Computing Machinery
T2 - 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018
Y2 - 2 July 2018 through 4 July 2018
ER -