An empirical study of iterative improvement in programming assignments

Raymond Pettit, John Homer, Roger Gee, Adam Starbuck, Susan Mengel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.

Original languageEnglish
Title of host publicationSIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education
EditorsCarl Alphonce, Adrienne Decker, Kurt Eiselt, Jodi Tims
PublisherAssociation for Computing Machinery, Inc
Pages410-415
Number of pages6
ISBN (Electronic)9781450329668
DOIs
StatePublished - Feb 24 2015
Event46th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2015 - Kansas City, United States
Duration: Mar 4 2015Mar 7 2015

Publication series

NameSIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education

Conference

Conference46th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2015
CountryUnited States
CityKansas City
Period03/4/1503/7/15

Keywords

  • Automated assessment tools
  • Automated feedback
  • Computer aided instruction
  • Computer science education

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