This study set out to provide a descriptive yet critical exploration of teachers’ experiences while using e-learning in the context of the 2019 novel coronavirus (COVID-19) pandemic. Using a qualitative phenomenology research paradigm, the study explored first-hand experiences of three university teachers (hence researchers as well) from two countries, that is, Bangladesh and Saudi Arabia. The contexts in which the researchers used e-learning showcased complex, challenging, and dynamic sites, that is, within institutions and individual classrooms. More specifically, the study identified acceptances, struggles, and negotiations at both the macro-level of policy/decision making and the micro-level of online classroom practices. Reflecting on the findings, this article concludes by offering a set of recommendations that might be applicable and useful for similar contexts beyond Bangladesh and Saudi Arabia. The researchers argue for developing a context-based, inclusive, and appropriate e-learning policy guideline that could be utilized during the emergency time now and in the near future.
Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.
In today’s economic world, the advancement in technology has opened up new forms of economic activities, particularly business. Whilst entrepreneurship is a major factor in business, e-entrepreneurship has become a buzzword facilitated by the rapid advancement of internet and developments in Information and Communication Technologies (ICTs). E-entrepreneurship, in the name of transforming business from the local marketplace to the global one, has revolutionized the entire business processes. This set of new business mechanism has created new opportunities for the startups, which in this regard is termed as e-startups. The purpose of this paper, therefore, is to develop a comprehensive understanding of the concept of eentrepreneurship by addressing related potentials and challenges. Extant literature has been reviewed to this end. The analysis indicated that flexibility of and accessibility to technology and products, less capital and risk in comparison to physical businesses are the major advantages that an e-entrepreneur might enjoy while commencing an e-startup. On the other hand, lack of institutional support, digital security threat, tough competition with established brands, less innovation and lack of academic and practical exposure in terms of business and marketing are some barriers that challenge the operation of e-startups. The conclusion of the paper draws on some recommendations accordingly.
In recent years, Learning to Rank has not only shown effectiveness and better suitability for modern Web Era needs, but also has proved that it outperforms traditional ranking in terms of accuracy and efficiency. Evolutionary approach to Learning to Rank such as RankGP [37] and RankDE [3] have shown further improvement over non-evolutionary algorithms. However when Evolutionary algorithms have been applied to a large volume of data, often they showed they required so much computational efforts that they were not worth applying to industrial applications. In this thesis, we present RankGPES: a Learning to Rank algorithm based on a hybrid approach combining Genetic Programming with Evolution Strategies. Our results not only showed that it outperformed both RankGP [37] by 20% and RankDE [3] by 6% in terms of accuracy but also it showed it required significant less amount of time to converge to a near-optimal result.
Mobile learning (m-learning) adoption has incredibly increased with the implementation of related computing paradigms. The mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations with m-learning actors’ changing requirements. IoT, edge, mobile edge, fog, AI, and 5G, bring numerous features and increase m-learning efficiency across educational disciplines. This study investigates the state-of-the-art m-learning architectures, determines a unified m-learning MCA, and explores the related computing paradigms’ characteristics to expand m-learning provision. Also, it evaluates m-learning performance across the MCAs and the emerging computing architectures. It finds the four physical layer’s MCAs and several application layer’s m-learning architectures. Only distance-cloud MCA does explore, and the other three MCAs do ignore by experts. Besides, the performance evaluation in related computing paradigms gives terrific benefits and QoE. MEC offers ultra-low latency for resource-intensive m-learning applications, fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G Ultra-Wideband services are more significant for intelligent video analytics. Eventually, it identifies the challenges, limitations, presents implications, and raises the future research directions to improve m-learning performance efficiency. The study’s findings help m-learning actors, institutions, and potential stakeholders by following their needs.
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