Performance and consistency in learning to program

Alireza Ahadi, Raymond Lister, Shahil Lal, Juho Leinonen, Arto Hellas

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

15 Citations (Scopus)

Abstract

Performance and consistency play a large role in learning. Decreasing the effort that one invests into course work may have short-term benefits such as reduced stress. However, as courses progress, neglected work accumulates and may cause challenges with learning the course content at hand. In this work, we analyze students' performance and consistency with programming assignments in an introductory programming course. We study how performance, when measured through progress in course assignments, evolves throughout the course, study weekly uctuations in students' work consistency, and contrast this with students' performance in the course final exam. Our results indicate that whilst uctuations in students' weekly performance do not distinguish poor performing students from well performing students with a high accuracy, more accurate results can be achieved when focusing on the performance of students on individual assignments which could be used for identifying struggling students who are at risk of dropping out of their studies.

Original languageEnglish
Title of host publicationACE 2017
Subtitle of host publicationProceedings of the 19th Australasian Computing Education Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages11-16
Number of pages6
ISBN (Electronic)9781450348232
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventAustralasian Computing Education Conference (19th : 2017) - Geelong, Australia
Duration: 31 Jan 20173 Feb 2017

Conference

ConferenceAustralasian Computing Education Conference (19th : 2017)
Abbreviated titleACE 2017
Country/TerritoryAustralia
CityGeelong
Period31/01/173/02/17

Keywords

  • source code snapshot analysis
  • educational data mining
  • CS1

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