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Impact of Class Size on Student Evaluations for Traditional and Peer Instruction Classrooms

Published: 08 March 2017 Publication History

Abstract

As student enrollments in computer science increase, there is a growing need for pedagogies that scale. Recent evidence has shown Peer Instruction (PI) to be an effective in-class pedagogy that reports high student satisfaction even with large classes. Yet, the question of the scalability of traditional lecture versus PI is largely unexplored. To explore this question, this work examines publicly available student evaluations of computer science courses across a wide range of class sizes (50--374 students) over a four year period. It first compares evaluations regardless of size and confirms prior work that PI classes are better appreciated by students than traditional lecture. It then examines how course evaluations change with class size and provides evidence that PI achieves a smaller decline in evaluations as class size increases.

References

[1]
D. Allen and K. Tanner. Infusing active learning into the large-enrollment biology class: seven strategies, from the simple to complex. CBE, 4(4), 2005.
[2]
T. Andrews, M. Leonard, C. Colgrove, and S. Kalinowski. Active learning not associated with student learning in a random sample of college biology courses. CBE-Life Sci Educ, 10(4), 2011.
[3]
P. Blatchford. The Class Size Debate. 2003.
[4]
M. Borrego, J. E. Froyd, C. Henderson, and S. Cutler. Influence of engineering instructors' teaching and learning beliefs on pedagogies in engineering science courses. IJEE, 2013.
[5]
Course And Professor Evaluations. https://cape.ucsd.edu.
[6]
C. H. Crouch and E. Mazur. Peer instruction: Ten years of experience and results. AJP, 69, 2001.
[7]
D. Ebert-May, C. Brewer, and S. Allred. Innovation in Large Lectures: Teaching for Active Learning. Bioscience, 47(9), Oct. 1997.
[8]
S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth. Active learning increases student performance in science, engineering, and mathematics. PNAS, 111(23), June 2014.
[9]
G. J. Har tt and A. Tsui. An examination of class size reduction on teaching and learning processes: A theoretical perspective. BERA, 41(5), 2015.
[10]
D. A. Lake. Student performance and perceptions of a lecture-based course compared with the same course utilizing group discussion. Physical Therapy, 81(3), 2001.
[11]
E. Lazowska, E. Roberts, and J. Kurose. Student Interest in Computer Science. 2014.
[12]
C. B. Lee. Experience report: Cs1 in matlab for non-majors, with media computation and peer instruction. SIGCSE, 2013.
[13]
C. B. Lee, S. Garcia, and L. Porter. Can peer instruction be effective in upper-division computer science courses? TOCE, 13(3), Aug. 2013.
[14]
D. J. Nicol and J. T. Boyle. Peer Instruction versus Class-wide Discussion in Large Classes: A comparison of two interaction methods in the wired classroom. Studies in Higher Education, 28(4), Aug. 2010.
[15]
Pandas. http://pandas.pydata.org.
[16]
L. Porter, C. Bailey Lee, and B. Simon. Halving fail rates using peer instruction: A study of four computer science courses. SIGCSE, 2013.
[17]
L. Porter, C. Bailey-Lee, B. Simon, Q. Cutts, and D. Zingaro. Experience report: A multi-classroom report on the value of peer instruction. ITiCSE, 2011.
[18]
L. Porter, D. Bouvier, Q. Cutts, S. Grissom, C. Lee, R. McCartney, D. Zingaro, and B. Simon. A multi-institutional study of peer instruction in introductory computing. ACM Inroads, 7(2), 2016.
[19]
L. Porter, S. Garcia, J. Glick, A. Matusiewicz, and C. Taylor. Peer instruction in computer science at small liberal arts colleges. ITiCSE, 2013.
[20]
L. Porter and B. Simon. Retaining nearly one-third more majors with a trio of instructional best practices in cs1. SIGCSE, 2013.
[21]
E. E. Prather, A. L. Rudolph, G. Brissenden, and W. M. Schlingman. A national study assessing the teaching and learning of introductory astronomy. Part I. The e ect of interactive instruction. AJP, 77(4), Apr. 2009.
[22]
M. Sahami and C. Piech. As CS Enrollments Grow, Are We Attracting Weaker Students? SIGCSE, 2016.
[23]
M. I. Shuster and R. Preszler. Introductory biology course reform: A tale of two courses. volume 8, 2014.
[24]
B. Simon, M. Kohanfars, J. Lee, K. Tamayo, and Q. Cutts. Experience report: Peer instruction in introductory computing. SIGCSE, 2010.
[25]
B. Simon, J. Parris, and J. Spacco. How we teach impacts student learning: Peer instruction vs. lecture in CS0. SIGCSE, 2013.
[26]
S. Zweben and B. Bizot. 2014 Taulbee Survey. 27(5), May 2015.

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  1. Impact of Class Size on Student Evaluations for Traditional and Peer Instruction Classrooms

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    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
    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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    Author Tags

    1. class size
    2. peer instruction
    3. student evaluations

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