Tool Use in Computer-Based Learning Environments: Adopting and Extending the Technology Acceptance Model

Abstract

This study adopts the Technology Acceptance Model (TAM) and extends it to study the effects of different variables on tool use. The influence of perceptions on tool use was studied in two different conditions: with and without explanation of the tool functionality. As an external variable, self-efficacy was entered in the TAM and the main research question thus focused on the mediating effects of perceptions (perceived tool functionality and perceived tool usability) between self-efficacy on the one hand and quantity and quality of tool use on the other. Positive effects of perceived usability on perceived functionality were hypothesized as well as positive effects of quantity and quality of tool use on performance. Positive effects were expected in the condition with explanation of the tool functionality. Ninety-three university students were provided with concept maps as the learning tools within a hypertext. Using path analysis, we found—similar to the TAM—a significant positive relationship between perceived usability and perceived functionality. Whereas perceived usability had a positive influence on the quantity of tool use, which positively influenced performance, perceived functionality had a negative influence on quantity of tool use. Self-efficacy showed a relationship with perceived usability only with the explained functionality condition.

The Relationship Between Acceptance, Actual Use of a Virtual Learning Environment and Performance: An Ecological Approach

“…The positive relationship between PU and actual use are in line with former studies. Previous acceptance studies in the context of virtual learning environments also emphasized the importance of PU for the actual use of the virtual learning environment (Islam, 2013, Juarez Collazo et al, 2014van Raaj & Schepers, 2008;Selim, 2003). The results correspond with the literature on the cognitive mediational paradigm which suggest that students should perceive the functionality of the virtual learning environment, in order to use it.…”

“…Furthermore, using BI or selfreported use as the main dependent variables in prior research blinded researchers to other important effects, such as, students' performance (Benbasat & Barki, 2007). Juarez Collazo, Wu, Elen and Clarebout (2014) were pioneers in studying the relationship between students' acceptance and the quantity of tool use in computerbased learning environment, on performance. Tool use was defined as non-embedded support devices whose use depends on the students' initiative (i.e., using the tools depends on the students' action; Clarebout & Elen, 2006).…”

“…Taking TAM as a baseline, we used PU and PEOU as indicators of students' acceptance, and we assumed a direct effect of PEOU on PU. Based on previous research, we assumed that PU and PEOU affects actual use (Islam, 2013;Juarez Collazo et al, 2014;Selim, 2003;van Raaj & Schepers, 2008). Secondly, the effect of the students' acceptance and actual use of the virtual learning environment on students' performance was studied.…”

Are They Learning or Playing? Students’ Perception Traits and Their Learning Self-Efficacy in a Game-Based Learning Environment

“…It has been found that when a student has a higher acceptance of a new learning technology (environment), a learning behavior is more likely to form and be sustained. During the past decade, many TAMs have been studied and empirically validated (Cheon et al., 2015; Juarez Collazo et al., 2014; Tarhini et al., 2016). As one of the most cited studies, the TAM of Yi and Hwang (2003) revised previous TAMs by adding three intrinsic motivation variables: learning goal orientation, enjoyment, and self-efficacy .…”

“…To explore how and why students engage in meaningful learning in technologyenhanced and game-based learning environments, previous studies have developed two sets of theoretical models: (a) technology acceptance model (TAM; Cheon, Chung, & Lee, 2015;Juarez Collazo, Wu, Elen, & Clarebout, 2014;Tarhini, Hone, Liu, & Tarhini, 2016;Yi & Hwang, 2003) and (b) model of flow in game-based learning (MFGL; Kiili, 2005;Pavlas, 2010). The TAM depicts factors that affect students' acceptance of technology (Yi & Hwang, 2003).…”