CCVis: Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data

Simple item page
dc.contributor.authorGoulden, Maggie Celeste
dc.contributor.authorGronda, Eric
dc.contributor.authorYang, Yurou
dc.contributor.authorZhang, Zihang
dc.contributor.authorTao, Jun
dc.contributor.authorWang, Chaoli
dc.contributor.authorDuan, Xiaojing
dc.contributor.authorAmbrose, G. Alex
dc.contributor.authorAbbott, Kevin
dc.contributor.authorMiller, Patrick
dc.date.accessioned2018-12-14T16:01:17Z
dc.date.available2018-12-14T16:01:17Z
dc.description.abstractAs more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.en_US
dc.description.sponsorshipThis work was supported in part by the U.S. National Science Foundation through grants IIS-1455886, IIS-1560363, and DUE-1833129.en_US
dc.description.urihttps://www3.nd.edu/~cwang11/research/vda19-ccvis.pdfen_US
dc.format.extent11 pagesen_US
dc.genreresearch papersen_US
dc.identifierdoi:10.13016/M2XG9FF8B
dc.identifier.urihttp://hdl.handle.net/11603/12262
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectCCVis (Course Clickstream Visualization)en_US
dc.subjectrelationship between student clickstream behaviors and their course performance.en_US
dc.subjectanalyzing course clickstream dataen_US
dc.subjectanalyzing student online learning behaviorsen_US
dc.titleCCVis: Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Dataen_US
dc.typeTexten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
vda19-ccvis.pdf
Size:
2.14 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Student Collection