Impacts of Mobile Cloud Learning (MCL) on Blended Flexible Learning (BFL)

Gurung, Rashmi; Alsadoon, Abeer; Prasad, P. W. C.; Elchouemi, A.

doi:10.1109/dt.2016.7557158

Cited by 15 publications

(18 citation statements)

References 9 publications

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“…Moreover, the ANN makes it possible to perform more precise anticipation in comparison to the general regression procedure [173]. However, some researchers applied a combination of ANN and PLS-SEM analysis in diverse adoption settings, such as CRM adoption Ahani, Rahim [174], CC Sharma, Al-Badi [67], and wearable healthcare devices and IOS adoption Chong and Bai [175]. Therefore, at the main phase, to find the factors that have an important influence on the adoption of CC, this study applied SEM.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, various factors are affecting this decision, and in the present study, HEI is the entity while CC adoption is the object. After analyzing previous studies, it was found that many studies considered constructs influencing the CC adoption at an individual level ( [24,[59][60][61]63,[65][66][67][68][69][70][71][72][73][74][75][76][77]); however, there was a dearth of material concerning this at the organizational level [31,32,35]. As mentioned above, the two most dominant hypotheses used for considering technology adoption from an organizational perspective are the TOE framework, and the DOI model [31,32,35,[78][79][80][81][82][83][84]].…”

Section: Technology Adoption Theoriesmentioning

confidence: 99%

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A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

et al. 2020

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Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions.

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Section: Methodsmentioning

confidence: 99%

Section: Technology Adoption Theoriesmentioning

confidence: 99%

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

et al. 2020

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“…availability and reputation) in their adaptation process. It is a mobile application for primary school students to learn the basic concepts in a specific language named Mudahnya BM., an Android based application that follows MCL recursive model from Gurung et al (2016) and Wang et al (2016) that comprises four main elements which are learning, assessment, feedback, and performance. This application considers learner contexts such as their age and mark, whereas device context that have been considered are network status and battery level.…”

Section: Discussionmentioning

confidence: 99%

“…Whereas the combination of the techniques in semantic-based approach enriches the context representation of the services and contexts during the reasoning process to find the equivalence services, Harchay et al (2015), Gomez et al (2014). Apart from that, learners can get easily distracted if it takes a long time to load and display the learning content, Gurung et al (2016), Wang et al (2016). Hence, there is a need to consider the network status, battery level, and QoS in terms of their services availability and reputation to retrieve the Learning Resources (LRs) (Wang et al, 2014).…”

Section: Revised Manuscript Received Onmentioning

confidence: 99%

The Dynamic Web Services Adaptation Framework in Context-Aware Mobile Cloud Learning Using Semantic-Based Method

Muhammad¹,

Admodisastro²,

Osman³

et al. 2019

IJEAT

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Most of Service-Based Applications (SBAs) have to be changed after their first deployment not solely due to the changing system requirements as well as of continuous change of the environment itself. With the growth of web service paradigm, there is a need for an efficient mechanism in dynamic adaptation process to offer users a better service experience. In particular, context-aware can be adapted in dynamic adaptation by considering user's contexts and device's contexts. Context awareness will be a key area for Mobile Cloud Learning (MCL) environment as it helps in the reasoning process to provide correct education resources for learners' prospect. Contextual information are represented using semantic-based approach for high expressiveness and formal representation for reasoning technique. However, dynamic adaptations frameworks in previous research are still lacking in terms of contextual information of the learner and device, and quality of services (QoS) were not considered. Besides, there is limited support of semantic expressiveness in terms of contextual information and services by using solely semantic-based technique. This paper proposes Dynamic Adaptation in Context Aware Mobile Cloud Learning (DACAMoL) framework to support adaptation process in MCL using semantic-based approach. The framework is applied in MCL mobile application that offers basic learning language.

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“…Hence CBML is very effective to boost the creative performance of learners. Study [86] has proposed a mobile cloud framework for blended flexible learning that consists of access management, support for personalized, collaborative and micro learning, multimedia content, learner modeling, a student progress tracker and parent involvement. The framework does not incorporate the technical implementation aspects.…”

Section: Csfs For Cloud-based Mobile Learningmentioning

confidence: 99%

Developing a Cloud-Based Mobile Learning Adoption Model to Promote Sustainable Education

2020

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Education plays a very significant role in the context of sustainability. As the world population is growing, providing education through the traditional classroom setting is not sufficient and not feasible to extend learning in professional life. Therefore, modern technology-mediated learning paradigms such as mobile learning are becoming increasingly popular. Mobile learning is said to integrate multiple contexts, learning types, mobilities and communications. As information and communications technology (ICT) plays a vital role in the delivery of mobile learning services, it is very essential to adopt sustainable IT resources to keep it viable. Cloud computing offers a range of affordable, scalable and on-demand solutions. This paper attempts to model important critical success factors (CSFs) in the area of cloud-based mobile learning using the interpretive structural modeling (ISM) technique. ISM helps in identifying the hierarchical inter-relationships between the variables of study with the help of experts in the field. Finally, Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) analysis is employed to classify the variables into dependent and independent variables. Management support has been identified as most rudimentary among sixteen CSFs identified through a literature review to establish a distinguished relative advantage. Further, the paper discusses the theoretical underpinning of all the constructs. This study will help organizations to implement mobile learning in sustainable ways.

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Impacts of Mobile Cloud Learning (MCL) on Blended Flexible Learning (BFL)

Cited by 15 publications

References 9 publications

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

The Dynamic Web Services Adaptation Framework in Context-Aware Mobile Cloud Learning Using Semantic-Based Method

Developing a Cloud-Based Mobile Learning Adoption Model to Promote Sustainable Education

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