Information and communication technologies (ICT) make blended learning possible. Unlike traditional classroom learning, the success of blended learning environments depends also on students’ acceptance and use of the technologies involved as well as course design.  Technology acceptance model (TAM) is a well-known and well-researched model that explains users’ technology acceptance or rejection behaviors through multiple variables (Agarwal & Prasad, 1999). The model investigates the relationship between behavioral intention to use a technology (BI) and actual use (Davis, 1989). Stemming from Ajzen and Fishbein’s (1980) theory of reasoned action, TAM asserts that perceived usefulness (PU) and perceived ease of use (PEU) of a technology affect users’ attitude towards use (AT), and consequently, AT determines BI (Davis, 1989). Self-efficacy (SE) and perceived enjoyment (PE) are among the most frequently used factors to extend TAM.

             The literature suggests that the effectiveness of learning environments using both synchronous and asynchronous technologies depends on providing instructional, social, and cognitive presence and maintaining a balance between them (Garrison, 2007). Instructional presence covers a wide range of instructional components such as learning environment design, instructional activities, managing discussions, providing feedback, and evaluation. Social presence is the degree to which the learners reflect their personalities in the online environments using the available means of interaction. Cognitive presence occurs when learners engage in cognitive activities that support meaningful learning such as deep thinking and discussion. Considering the influences of the three types of presence on blended learning experience, it is assumed that they also have effects on learners’ technology acceptance and use as well (Akyol & Garrison, 2011).

             Interactions are of importance to elicit learners’ satisfaction in synchronous and asynchronous learning environments alike.  According to Moore and Kearsley (1996), learning environments involve three types of interactions, namely student - student, student - teacher, and student - content. Student – student and student – teacher interactions require more than one individual to happen. Student – student interaction covers students’ information exchanges regarding course work. On the other hand, student – teacher interaction includes students seeking feedback, help, and technical support from their teachers.

             The purpose of this study was to investigate the direct and indirect effects of presence and interactions on the technology acceptance factors of PU, PEU, AT, SE, and PE. 

The participants of the study were 345 first year university students enrolled in an introductory computer education course. The participants received course content including instructional videos and assignments via Google Classroom –a course management system available to G Suite for Education users- throughout the semester (15 weeks). Three data collection tools were employed in the study: the online presence scale, Boston et al.’s, 2009, the interaction scale (Kuoa, Bellandb, Schroderc, & Walker, 2014), and the technology acceptance scale (Ursavaş et al., 2015). The data were analyzed using structural equation modeling. All fit indices indicated an acceptable fit. The structure model confirmed a complex, yet expected, relationship structure among the variables.

Student – content interaction had direct effects on SE, PE and PU. It also affected AT, PEU and BI indirectly. Instructional presence had a direct influence only on PE, while it indirectly influenced AT and BI. PE had direct effects on AT and BI, and indirect effects on PU and BI. The remaining direct effects were confirmations of the original TAM. Specifically, SE had a direct effect over PEU and AT, PEU affected PU, AT affected BI and PEU, and finally PU affected BI. Based on the findings, it can be said the teachers’ directives regarding the topic or the course may influence learners’ satisfaction with the course, attitudes towards technology, and behavioral intention to use technology. Furthermore, learners’ interactions with the course content in a blended learning environment may have an impact on their self-efficacy beliefs about using technology, perceptions of ease of use and usefulness of blended learning technologies, satisfaction with the learning experience, attitude towards such technologies, and intention to use these technologies. The findings suggest that course designer can improve the effectiveness of blended learning environments by developing well-organized and ease to use course content and providing meaningful interactions to the learners. Such a course design will also benefit learners’ behavioral intention to use blended learning technologies.

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