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Digital Learning Analytics Recommender System for Universities

Digital Learning Analytics Recommender System for Universities

Hadeel Alharbi, Kamaljeet Sandhu
Copyright: © 2020 |Pages: 16
ISBN13: 9781799851714|ISBN10: 1799851710|ISBN13 Softcover: 9781799851721|EISBN13: 9781799851738
DOI: 10.4018/978-1-7998-5171-4.ch010
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MLA

Alharbi, Hadeel, and Kamaljeet Sandhu. "Digital Learning Analytics Recommender System for Universities." Digital Innovations for Customer Engagement, Management, and Organizational Improvement, edited by Kamaljeet Sandhu, IGI Global, 2020, pp. 184-199. https://doi.org/10.4018/978-1-7998-5171-4.ch010

APA

Alharbi, H. & Sandhu, K. (2020). Digital Learning Analytics Recommender System for Universities. In K. Sandhu (Ed.), Digital Innovations for Customer Engagement, Management, and Organizational Improvement (pp. 184-199). IGI Global. https://doi.org/10.4018/978-1-7998-5171-4.ch010

Chicago

Alharbi, Hadeel, and Kamaljeet Sandhu. "Digital Learning Analytics Recommender System for Universities." In Digital Innovations for Customer Engagement, Management, and Organizational Improvement, edited by Kamaljeet Sandhu, 184-199. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-5171-4.ch010

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Abstract

The aim of this chapter is to present the multivariate analyses results of the factors that influence students' acceptance and the continuance usage intention of digital learning analytics recommender systems at higher education institutions in Saudi Arabia. Data was collected from 353 Saudi Arabian university students via an online digital survey questionnaire. The research model was then used to examine the hypothesized relationships between user experiences of the digital learning analytics recommender system and their intentions for long-term adoption of the system. The research model was primarily based on the technology acceptance model (TAM) developed by Davis (1989)—the variables ‘perceived usefulness', ‘perceived ease of use', and ‘acceptance', particularly—with ‘continuance usage intention' added as an endogenous construct and with ‘service quality' and ‘user experience' added as external variables.

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