Digital Learning Analytics Recommender System for Universities

Digital Learning Analytics Recommender System for Universities

Hadeel Alharbi (University of New England, Australia) and Kamaljeet Sandhu (University of New England, Australia)
DOI: 10.4018/978-1-7998-5171-4.ch010

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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Research Model And Hypotheses

The research model to investigate the factors affecting students' acceptance and the continuance usage intention of digital learning analytics recommender systems is primarily based on the TAM developed by Davis (1988). It incorporates ‘service quality’ and ‘user experience’ as external variables; and includes the TAM variables: ‘perceived usefulness’, ‘perceived ease of use’, and ‘acceptance’. The research model also extended the TAM by adding ‘continuance usage intention’ as the ultimate endogenous construct in the developed model (see Figure1).

Figure 1.

Research Model for digital learning analytics recommender system acceptance

978-1-7998-5171-4.ch010.f01

Following analysis of the findings reported in previous studies (e.g. DeLone and McLean, 1992; Gorla et al., 2010; Rana et al., 2015) and in consideration of the key issues explored in the literature (e.g. Al-Amoush, and Sandhu 2020; Alharbi, and Sandhu 2019; Dağhan, and Akkoyunlu, 2016; Karapanos, 2013; Lin 2011; Lee, 2010), the following hypotheses were formulated:

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