Using learning analytics to investigate patterns of performance and engagement in large classes

Khosravi, Hassan and Cooper, Kendra (2017). Using learning analytics to investigate patterns of performance and engagement in large classes. In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. SIGCSE '17, Seattle, WA, United States, (309-314). 8-11 March 2017. doi:10.1145/3017680.3017711


Author Khosravi, Hassan
Cooper, Kendra
Title of paper Using learning analytics to investigate patterns of performance and engagement in large classes
Conference name SIGCSE '17
Conference location Seattle, WA, United States
Conference dates 8-11 March 2017
Convener SIGCSE
Proceedings title Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
Journal name Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE
Place of Publication New York, NY, United States
Publisher ACM
Publication Year 2017
Sub-type Fully published paper
DOI 10.1145/3017680.3017711
Open Access Status Not yet assessed
ISBN 9781450346986
Volume Part F126972
Start page 309
End page 314
Total pages 6
Language eng
Abstract/Summary Educators continue to face significant challenges in providing high quality, post-secondary instruction in large classes including: motivating and engaging diverse populations (e.g., academic ability and backgrounds, generational expectations); and providing helpful feedback and guidance. Researchers investigate solutions to these kinds of challenges from alternative perspectives, including learning analytics (LA). Here, LA techniques are applied to explore the data collected for a large, flipped introductory programming class to (1) identify groups of students with similar patterns of performance and engagement; and (2) provide them with more meaningful appraisals that are tailored to help them effectively master the learning objectives. Two studies are reported, which apply clustering to analyze the class population, followed by an analysis of a subpopulation with extreme behaviours.
Keyword Learning analytics
Personalizing learning
Clustering
CS1
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Tue, 28 Mar 2017, 07:08:56 EST by Hassan Khosravi on behalf of Institute for Teaching and Learning Innovation