Learning loops: A replication study illuminates impact of HS courses

Briana B. Morrison, Adrienne Decker, Lauren E. Margulieux

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

20 Scopus citations

Abstract

A recent study about the effectiveness of subgoal labeling in an introductory computer science programming course both supported previous research and produced some puzzling results. In this study, we replicate the experiment with a different student population to determine if the results are repeatable. We also gave the experimental task to students in a follow-on course to explore if they had indeed mastered the programming concept. We found that the previous puzzling results were repeated. In addition, for the novice programmers, we found a statistically significant difference in performance based on whether the student had previous programming courses in high school. However, this performance difference disappears in a follow-on course after all students have taken an introductory computer science programming course. The results of this study have implications for how quickly students are evaluated for mastery of knowledge and how we group students in introductory programming courses.

Original languageEnglish (US)
Title of host publicationICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research
PublisherAssociation for Computing Machinery, Inc
Pages221-230
Number of pages10
ISBN (Electronic)9781450344494
DOIs
StatePublished - Aug 25 2016
Event12th Annual International Computing Education Research Conference, ICER 2016 - Melbourne, Australia
Duration: Sep 8 2016Sep 12 2016

Publication series

NameICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research

Other

Other12th Annual International Computing Education Research Conference, ICER 2016
Country/TerritoryAustralia
CityMelbourne
Period9/8/169/12/16

Keywords

  • Cognitive Load
  • Contextual Transfer
  • Subgoal labels

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Software
  • Education

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