Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK

Hafsa Al Ansari, Rupert Ward, Richard Hill

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

Abstract

This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.
Original languageEnglish
Title of host publicationProceedings 21st IEEEE International Conference on Advanced Learning Technologies
Subtitle of host publicationICALT 2021
EditorsMaiga Chang, Nian-Shing Chen, Demitrios G Sampson, Ahmed Tlili
PublisherIEEE
Pages442-444
Number of pages3
ISBN (Electronic)9781665441063
ISBN (Print)9781665431163
DOIs
Publication statusPublished - 2 Aug 2021
Event21st IEEE International Conference on Advanced Learning Technologies - Online, Virtual
Duration: 12 Jul 202115 Jul 2021
Conference number: 21
https://tc.computer.org/tclt/icalt2021/

Publication series

NameProceedings (IEEE International Conference on Advanced Learning Technologies)
PublisherIEEE
ISSN (Print)2161-3761
ISSN (Electronic)2161-377X

Conference

Conference21st IEEE International Conference on Advanced Learning Technologies
Abbreviated titleICALT 2021
CityVirtual
Period12/07/2115/07/21
Internet address

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