Predicting student success using fine grain clicker data

Porter, L; Zingaro, D; Lister, R

Predicting student success using fine grain clicker data

Porter, L Zingaro, D Lister, R

Permalink

Publication Type:: Conference Proceeding
Citation:: ICER 2014 - Proceedings of the 10th Annual International Conference on International Computing Education Research, 2014, pp. 51 - 58
Issue Date:: 2014-01-01

Closed Access

	Filename	Description	Size
	2014_08_ICER_PorterZingaroAndLister2014PredictingStudentSuccessUsingFineGrainClickerData_AsItAppearedInTheProceedings.pdf	Published version	431.31 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Porter, L	en_US
dc.contributor.author	Zingaro, D	en_US
dc.contributor.author	Lister, R https://orcid.org/0000-0001-5780-0445	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	ICER 2014 - Proceedings of the 10th Annual International Conference on International Computing Education Research, 2014, pp. 51 - 58	en_US
dc.identifier.isbn	9781450327558	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33411
dc.description.abstract	Recent research suggests that the first weeks of a CS1 course have a strong inuence on end-of-course student performance. The present work aims to refine the understanding of this phenomenon by using in-class clicker questions as a source of student performance. Clicker questions generate per-lecture and per-question data with which to assess student under- standing. This work demonstrates that clicker question per- formance early in the term predicts student outcomes at the end of the term. The predictive nature of these questions applies to code-writing questions, multiple choice questions, and the final exam as a whole. The most predictive clicker questions are identified and the relationships between these questions and final exam performance are examined. Copyright © 2014 ACM.	en_US
dc.relation.ispartof	ICER 2014 - Proceedings of the 10th Annual International Conference on International Computing Education Research	en_US
dc.relation.isbasedon	10.1145/2632320.2632354	en_US
dc.title	Predicting student success using fine grain clicker data	en_US
dc.type	Conference Proceeding
utslib.for	130212 Science, Technology and Engineering Curriculum and Pedagogy	en_US
utslib.for	089999 Information and Computing Sciences not elsewhere classified	en_US
dc.location.activity	Glasgow, United Kingdom
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
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recent research suggests that the first weeks of a CS1 course have a strong inuence on end-of-course student performance. The present work aims to refine the understanding of this phenomenon by using in-class clicker questions as a source of student performance. Clicker questions generate per-lecture and per-question data with which to assess student under- standing. This work demonstrates that clicker question per- formance early in the term predicts student outcomes at the end of the term. The predictive nature of these questions applies to code-writing questions, multiple choice questions, and the final exam as a whole. The most predictive clicker questions are identified and the relationships between these questions and final exam performance are examined. Copyright © 2014 ACM.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33411