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A Preliminary Model of Learning Analytics to Explore Data Visualization on Educator�s Satisfaction and Academic Performance in Higher Education

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dc.contributor.authorShahril Khuzairi N.M.en_US
dc.contributor.authorChe Cob Z.en_US
dc.contributor.authorid57361333500en_US
dc.contributor.authorid25824919900en_US
dc.date.accessioned2023-05-29T09:10:41Z
dc.date.available2023-05-29T09:10:41Z
dc.date.issued2021
dc.descriptionComputer aided instruction; Decision making; Digital storage; E-learning; Education computing; Learning systems; Online systems; Students; Teaching; Visualization; Academic performance; Decisions makings; High educations; IS success; IS success model; Learning analytic; Models of learning; Online learning; Preliminary model; Technology acceptance model; Data visualizationen_US
dc.description.abstractWith the rapid proliferation of online learning due to the Covid-19 pandemic, learning management solutions and software has gained an extraordinary importance in tertiary education. This shift has created large amounts of data from online learning systems that need to be translated into meaningful information, hence data visualization has come into prominent focus as a solution that provides a powerful means to drive Learning Analytics to assess and support educators and students alike in decision-making and sense-making activities from the data collected. Although many research works have been published on data visualization focusing on techniques, tools and best practices, there is still a lack of research in the context of online learning to meet this urgent need of quality data visualization for successful decision-making. In this paper, we explore data visualization that is currently used in learning analytics and present an integrated preliminary model based on DeLone and McLean�s IS Success model to examine the role and significance of data visualization by incorporating it as an antecedent to the Information Quality construct of the IS success model, which will support teaching and learning in an online learning environment for improved educators and student performance. This paper adds to the existing literature by incorporating data visualization to support educators decision-making and its performance impact of online learning through the consideration of the IS success model�s elements. This integrated preliminary conceptual model aims to support online teaching and learning by addressing the research gap that has emerged from the expansion of learning analytics in educational technology. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_3
dc.identifier.epage40
dc.identifier.scopus2-s2.0-85120531097
dc.identifier.spage27
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120531097&doi=10.1007%2f978-3-030-90235-3_3&partnerID=40&md5=7d73f6351202732bf3a1b68d1495bd82
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26451
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleA Preliminary Model of Learning Analytics to Explore Data Visualization on Educator�s Satisfaction and Academic Performance in Higher Educationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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