Exploring the value of different data sources for predicting student performance in multiple CS courses

Soohyun Nam Liao, Daniel Zingaro, Christine Alvarado, William G. Griswold, Leo Porter

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

3 Scopus citations

Abstract

A number of recent studies in computer science education have explored the value of various data sources for early prediction of students' overall course performance. These data sources include responses to clicker questions, prerequisite knowledge, instrumented student IDEs, quizzes, and assignments. However, these data sources are often examined in isolation or in a single course. Which data sources are most valuable, and does course context matter? To answer these questions, this study collected student grades on prerequisite courses, Peer Instruction clicker responses, online quizzes, and assignments, from five courses (over 1000 students) across the CS curriculum at two institutions. A trend emerges suggesting that for upper-division courses, prerequisite grades are most predictive; for introductory programming courses, where no prerequisite grades were available, clicker responses were the most predictive. In concert, prerequisites and clicker responses generally provide highly accurate predictions early in the term, with assignments and online quizzes sometimes providing incremental improvements. Implications of these results for both researchers and practitioners are discussed.

Original languageEnglish (US)
Title of host publicationSIGCSE 2019 - Proceedings of the 50th ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery, Inc
Pages112-118
Number of pages7
ISBN (Electronic)9781450358903
DOIs
StatePublished - Feb 22 2019
Event50th ACM Technical Symposium on Computer Science Education, SIGCSE 2019 - Minneapolis, United States
Duration: Feb 27 2019Mar 2 2019

Publication series

NameSIGCSE 2019 - Proceedings of the 50th ACM Technical Symposium on Computer Science Education

Conference

Conference50th ACM Technical Symposium on Computer Science Education, SIGCSE 2019
CountryUnited States
CityMinneapolis
Period2/27/193/2/19

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Education

Keywords

  • Architecture
  • CS1
  • CS2
  • Data Structures
  • Low-performing students
  • Machine learning
  • Prediction
  • Student outcomes

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