| Summary: | In order to explain and determine the attitudes and factors affecting perceptions of students to adopt and use Virtual Learning Environment (VLE) as a tool in complementing and supplementing face-to-face learning, this research combined two theoretical models: Technology Acceptance Model (TAM), one of the more popular acceptance models, and Learning Style Inventory (LSI). The Technology Acceptance Model is one of the models used to study the problem of low adoption or underutilization of technology while learning styles model adopted in order to determines the preferred learning styles for the users of VLE. This study investigates students at Tripoli University, the main University in Libya to understand their perceptions of using VLE with respect to their learning styles. The study used a quantitative descriptive research design method by using a survey as the primary means of data collection. Empirical data were collected from different departments and schools (n=302) to examine the impact of specialisation construct. The study proposed a conceptual model which includes external variables derived from previous research, the core TAM model combined with learning style as an independent variable in order to determine the impact of learning styles on the perception of students towards VLE use. A combination of t tests, ANOVAs, chi-squares, and Pearson’s product-moment correlation coefficients was used to analyse the data by using two techniques: single and multiple regressions. Findings from the quantitative data revealed that, regardless of gender or learning styles impacts, the participants have a strong positive behavioural intention to use VLE tools in their existing learning environment. The results of this study implied that gender and learning styles did not play a significant role in determining perceptions and usage of VLE. However, the other defined independent variables had significant effects on the model and contributed to the explanation of the model except for example, job relevance, complexity factors. The interesting result found in this study was the fact that the specialisation constructs shows that there is a different level of VLE use depending on the student’s specialisation, namely that natural and formal science students showed the most interest in using the new technology. Another interesting outcome found that students’ perceived ease of use demonstrated a more consistent influence compared to usefulness in determining the usage of VLE. This finding is new and is inconsistent with most previous research. Although, the results show that there is no significant impact of learning styles on the research model, the results, however, show learning styles can play a very important role as a moderating factor between beliefs constructs and external variables. The results of the coefficients were not the same for each learning style, which may indicate that different learning styles moderate the relationships between variables involved in the research model (VLEAM). The people with the highest coefficients were those with the assimilator style compared to other learning styles, followed by divergent/accommodator convergers. This means that assimilators are the best target learners for VLE. However, the results show that female assimilators have more negative impact on PU, meaning that they regard VLE as being less useful. The parameters in the model may be altered for each learning style to get the maximum benefit from the model. From a theoretical and methodological perspective, it was found that TAM being a simple psychological model was not good enough to explain broader systems such as VLE and subsequently has not fully explained students’ perceptions towards use. In the light of the findings, the study suggested that a study of adoption and acceptance technology should move from using a simple psychological TAM model to another form that is able to measure IS that contains complex functions. The outcomes of the study are beneficial to decision makers at the university level when making decisions about technologies that affect the teaching and learning process as well as assisting in institutional decision in regards to where to commit resources (technology, monetary, labour, etc.) to implement and maintain those systems.
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