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

Ahadi, A

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

Ahadi, A

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Publication Type:: Conference Proceeding
Citation:: ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research, 2016, pp. 263 - 264
Issue Date:: 2016-08-25

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Field	Value	Language
dc.contributor.author	Ahadi, A https://orcid.org/0000-0002-2018-3065	en_US
dc.date.available	2020-05-25T19:07:02Z
dc.date.issued	2016-08-25	en_US
dc.identifier.citation	ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research, 2016, pp. 263 - 264	en_US
dc.identifier.isbn	9781450344494	en_US
dc.identifier.uri	http://hdl.handle.net/10453/50421
dc.description.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.	en_US
dc.relation.ispartof	ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research	en_US
dc.relation.isbasedon	10.1145/2960310.2960339	en_US
dc.title	Early identification of novice programmers' challenges in coding using machine learning techniques	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

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.

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http://hdl.handle.net/10453/50421