A multi-level analysis of the relationship between instruc-tional practices and retention in computer science

Markeya S. Peteranetz, Leen Kiat Soh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Increasing retention in computer science (CS) courses is a goal of many CS departments. A key step to increasing retention is to un-derstand the factors that impact the likelihood students will con-tinue to enroll in CS courses. Prior research on retention in CS has mostly examined factors such as prior exposure to programming and students' personality characteristics, which are outside the control of undergraduate instructors. This study focuses on fac-tors within the control of instructors, namely, instructional prac-tices that directly impact students' classroom experiences. Partic-ipants were recruited from 25 sections of 14 different courses over 4 semesters. A multi-level model tested the effects of individual and class-average perceptions of cooperative learning and teacher directedness on the probability of subsequent enrollment in a CS course, while controlling for students' mastery of CS concepts and status as a CS major. Results indicated that students' individual perceptions of instructional practices were not associated with re-tention, but the average rating of cooperative learning within a course section was negatively associated with retention. Con-sistent with prior research, greater mastery of CS concepts and considering or having declared a CS major were associated with a higher probability of taking a future CS courses. Implications for findings are discussed.

Original languageEnglish (US)
Title of host publicationSIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery
Pages37-43
Number of pages7
ISBN (Electronic)9781450367936
DOIs
StatePublished - Feb 26 2020
Event51st ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2020 - Portland, United States
Duration: Mar 11 2020Mar 14 2020

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
ISSN (Print)1942-647X

Conference

Conference51st ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2020
CountryUnited States
CityPortland
Period3/11/203/14/20

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Keywords

  • Co-operative learning
  • Computer science education
  • Multi-level models
  • Retention
  • Student-centered classrooms

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Education

Cite this

Peteranetz, M. S., & Soh, L. K. (2020). A multi-level analysis of the relationship between instruc-tional practices and retention in computer science. In SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 37-43). (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). Association for Computing Machinery. https://doi.org/10.1145/3328778.3366812