In this article, we have identified and investigated the major framework characteristics and individual differences that impact the most important users’ perceptions about frameworks by using TAM. To test the causal relationships between these factors, we performed an online survey and analyzed the results using structural equation modeling. The results support the technology acceptance model (TAM). We found that framework characteristics and individual differences have a significant impact on users’ perceptions. Our results might be used to develop acceptable frameworks and for the evaluation of existing frameworks, their constituent parts and framework-related guidelines.
COBISS.SI-ID: 14599702
Existing literature in the field of e-learning technology acceptance reflects a significant number of independent studies that primarily investigate the causal relationships proposed by technology acceptance theory, such as the technology acceptance model (TAM). In this study, we have conducted a systematic literature review of 42 independent papers, mostly published in major journals and we have conducted a meta-analysis of the causal effect sizes between common TAM-related relationships. We gathered proof that: (1) TAM is the most-used acceptance theory in e-learning acceptance research, and (2) the size of the causal effects between individual TAM-related factors depends on the type of user and the type of e-learning technology. The results of our meta-analysis demonstrated a moderating effect for user-related factors and technology-related factors for several evaluated causal paths. We have gathered proof that the perceived ease of use and the perceived usefulness tend to be the factors that can influence the attitudes of users toward using an e-learning technology in equal measure for different user types and types of e-learning technology settings.
COBISS.SI-ID: 15270166
The complexity of study process at our faculty (several study programs, the dynamics of 220 teaching staff, more than 300 courses and over 2000 students) represents a great obstacle for our students who have to select mentors for their practical projects. Based on some very promising results of using semantic web technologies in education we developed a mentor selection support system, which is presented in this paper. Having the competences and skills regarding specific topics represented in a form of ontology-based semantic network enabled us to retrieve this information automatically. The results show some very positive aspects of using the system, both for students and teaching staff.
COBISS.SI-ID: 15625238