Empirical evidence on the e-learning adoption in the field of special education is quite limited. Path modelling in particular draws attention as an important methodological gap. Therefore, a model that can provide a theoretical basis for practice in special education has the potential to make significant contributions. Accordingly, this study aimed to identify the factors influencing the intention to use e-learning systems by proposing an extended technology acceptance model for special education. The participant group consisted of 1713 university students with special needs receiving education through the e-learning systems of a state university. For analysing the data, partial least squares-structural equation modelling was used. The results showed that the model explained 76.9% of intention to use e-learning systems. Perceived ease of use and perceived usefulness had the strongest relationship in the model while that between perceived enjoyment and behavioural intention represented the strongest relationship in terms of influence on intention. In addition, hypothesis tests revealed that both social and individual-emotional factors affected intention to use e-learning, and constructs that provide intrinsic motivation and constructs of extrinsic motivation associated with performance improvement play a critical role in e-learning adoption. Accordingly, implications for research and practice are discussed.
Implications for practice or policy:
It is critical for instructional designers, special education experts and policymakers to consider the effects of core acceptance constructs, both in terms of competence in and tendency to use e-learning systems.
E-learning system designs that can meet the expectations arising from social norms and can contribute to strengthening the sense of belonging may have a crucial role.
It is vital to consider the enjoyment elements in terms of ensuring quality learning through online education.
A total of 1023 selected articles published in 2016–2019 related to mobile learning were examined and classified according to the categories in this research: 40% of these articles used quantitative approaches, 18% of them used mixed, and 13% of them were literature reviews. The published studies were analyzed according to research model, sample size, sample level, learning fields, subject-area classification, data collection tool, data analysis technique, dependent variable, independent variable, mobile device, number of authors, and publication year. The findings were analyzed and interpreted as a percentage and frequency. This research will be useful for reviewing current research trends related to mobile learning studies, indicating potential research on the topics, and revealing the needs of the field.
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