The conventional education system in developing countries has been enhanced recently by implementing the latest technology of distributed ledger. Disruptive technology is a fundamental requirement for greater accountability and visibility. We explored the key factors affecting the intentions of educational institutions to use blockchain technology for e-learning. This study proposed an expanded model of Technology Acceptance Model by integrating the diffusion of innovation theory. Based on an online survey, the conceptual model was tested and validated using structural equation modeling. The results showed that compatibility had a significant impact on blockchain use in smart learning environments. Other significant effects were also found on adoption of blockchain technology. This study offers an expanded Technology Acceptance Model for implementing blockchain that could assist decision makers in building a smart learning environment for the educational institutes for the emerging economies.
Virtual learning environments have been recognized as an area of particular importance by which educators can use to improve desirable learning behaviours. Investigating the impact of different virtual environments on learners’ behaviours has become the centre of attention of researchers, especially during COVID-19. The homophily effect of avatar-identity on individuals’ perceptions of an environment can be a key for understanding their learning behaviours. This study examined the relationship between key constructs related to avatar homophily (background and attitude) and learners’ flow and exploratory behaviour. An online survey was distributed to 157 students (93 males and 64 females with age ranging from 19 to 21 years) who took part in an online learning activity using an avatar-mediated environment (Second Life). The results showed that users’ flow experience can be influenced by the function of perceived background and attitude homophily in an avatar-mediated environment. Flow experience was found to mediate the relationship between avatar homophily and learners’ exploratory behaviour. This study offers a conceptual understanding of the relationship between homophily and individual’s flow state.
The application of different virtual reality interventions has been recently gaining interest from researchers and practitioners. Due to the current COVID-19 pandemic, higher education institutions increased the use of these technologies to help create much value for educators and students. The relationship between individual experience and engagement in avatar-mediated environments has garnered empirical support, but still little is known about the mediating mechanisms underlying this relation. This study investigated the mediating role of interpersonal attraction (e.g., social, physical, and task attraction) in the link between individual experience and engagement in an avatar-mediated learning environment. A total of 112 participants (21–23 years; 73 male and 39 female) participated in online peer learning sessions in Second Life. After controlling for demographic covariates, the results showed that (1) individual experience was positively associated with students’ engagement; (2) social, physical, and task attraction was found to positively mediate the link between experience and engagement. The outcomes from this study provide valuable implications for promoting online learners’ engagement in avatar-mediated environments.
Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates.
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