Bodong Chen

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Notes: Ali. (2013). Factors influencing beliefs for adoption of a learning analytics tool



Citekey: @Ali2013

Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130–148.



In this paper, we propose and empirically validate a Learning Analytics Acceptance Model (LAAM) of factors influencing the beliefs of educators concerning the adoption a learning analytics tool. In particular, our model explains how the usage beliefs (i.e., ease-of-use and usefulness perceptions) about the learning analytics of a tool are associated with the intention to adopt the tool. In our study, we considered several factors that could potentially affect the adoption beliefs: i) pedagogical knowledge and information design skills of educators; ii) educators’ perceived utility of a learning analytics tool; and iii) educators’ perceived ease-of-use of a learning analytics tool. (p. 130)

the study is done with a sample of educators who experimented with a LOCO-Analyst tool. (p. 130)

Educators, thus, require a learning system that provides learning analytics on their online courses that are both comprehensive and informative. (p. 130)

While many approaches and tools for learning analytics have been proposed, there is limited empirical insights of the factors influencing potential adoption of this new technology. To address this research gap, we propose and empirically validate a Learning Analytics Acceptance Model (LAAM), which we report in this paper, to start building research understanding how the analytics provided in a learning analytics tool affect educators’ adoption beliefs. (p. 131)

First, pedagogical knowledge and information design skills of individuals can influence their perception of the usefulness of learning systems (Bratt, 2009a, 2009b). Furthermore, McFarland and Hamilton’s (2006) refined technology acceptance model recognize prior experience as one of the context factors that could potentially impact the perceived usefulness of a system. (p. 131)

Second, studies have shown that decisions to adopt technology-based systems are influenced by the evaluators’ perceived utility of such systems (Bratt, Coulson, & Kostasshuk, 2009). The Technology Acceptance Model (TAM) theory (Davis, 1989) – whose roots lie in Fishbein and Ajzen’s (1975) Theory of Reasoned Action – posits that perceived usage beliefs determine individual behavioral intentions to use a specific technology or service. (p. 131)

Third, the relation between the ease-of-use belief and intention to adopt is not very clear, however. Some studies suggest there is a direct association between ease-of-use and intention to adopt (Davis, 1989; Gefen, Pavlou, Rise, & Warkentin, 2002). Others fail to report such an association (Warkentin, Gefen, Pavlou, & Rose, 2002). Venkatesh, Morris, Davis, and Davis (2003), however, suggest that perceived ease-of-use indirectly influences intention to adopt through perceived usefulness. (p. 131)

  1. Learning analytics acceptance model – LAAM (p. 131)

Following Davis (1989), we can understand perceived usefulness as the degree to which an educator believes that using a specific online learning system will increase his/her task performance. Ease-of-use is the degree to which an educator expects the use of the learning system to be free of effort. We built a LAAM research model to investigate our research questions and hypotheses. (p. 131)

The model is built on the measurement items (questions) included in our survey instrument listed in Table 1 (the survey instrument is described in Section 3.2). (p. 131)