Early identification of novice programmers' challenges in coding using machine learning techniques

Alireza Ahadi*

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

It is well known that many first year undergraduate university students struggle with learning to program. Educational Data Mining (EDM) applies machine learning and statistics to information generated from educational settings. In this PhD project, EDM is used to study first semester novice programmers, using data collected from students as they work on computers to complete their normal weekly laboratory exercises. Analysis of the generated snapshots has shown the potential for early identification of students who later struggle in the course. The aim of this study is to propose a method for early identification of "at risk" students while providing suggestions on how they can improve their coding style. This PhD project is within its final year.

Original languageEnglish
Title of host publicationICER 2016
Subtitle of host publicationProceedings of the 2016 ACM Conference on International Computing Education Research
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages263-264
Number of pages2
ISBN (Electronic)9781450344494
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventAnnual International Computing Education Research Conference (12th : 2016) - Melbourne, Australia
Duration: 8 Sep 201612 Sep 2016

Conference

ConferenceAnnual International Computing Education Research Conference (12th : 2016)
Abbreviated titleICER 2016
CountryAustralia
CityMelbourne
Period8/09/1612/09/16

Keywords

  • machine learning
  • novice programmers
  • source code snapshot analysis

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