Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment

Hossain, M. Shamim; Nazir, Amril; Muhammad, Ghulam; Alamri, Atif

doi:10.1016/j.chb.2018.07.002

Cited by 115 publications

(72 citation statements)

References 17 publications

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“…Rapid advancements in Technology-Enhanced Learning platforms have shown a tremendous increase in online educational data to yield ample educational repositories (Treasure-Jones et al, 2019) -demonstrating significant impact on Higher Educational Institutions. This rapid increase in educational data is also providing opportunities to optimize users' engagement with technological platforms to enhance the learning experience (Shorfuzzaman et al, 2019). The progression of the accumulated educational data has stimulated the emergence of several research communities, such as learning analytics to predict learners' behavior and providing indicators for optimized policy formulations (Azcona & Smeaton, 2017;Viberg et al, 2018;Capuano & Toti 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

328

185

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Introductionmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

328

185

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“…The study [77] proposed to use CBML to manage the huge amount of data by using cloud-based Big Data capabilities and also offloading the heavy computing needs to the cloud. Further, the application of the localized technology acceptance model (TAM) in the study proved that personal innovativeness was found to be an influential factor for mobile learning adoption in addition to ease of use, external motivation from family and friends and financial factors.…”

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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“…Analytics use in higher education administration is often discussed in terms of big data that encompasses classroom data as well as data from campus card access and students' usernames for other online services a campus may provide (Aguilar, 2018;Long & Siemens, 2011;Shorfuzzaman, Hossain, Nazir, Muhammad, & Alamri, 2019;Wintrup, 2017). The term "big data" has become one of the educational buzzwords that encapsulates many meanings.…”

Section: Organizational Driversmentioning

confidence: 99%

A Framework to Support Interdisciplinary Engagement with Learning Analytics

Blackmon

Moore

2020

Adoption of Data Analytics in Higher Education Learning and Teaching

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Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment

Cited by 115 publications

References 17 publications

Predicting academic performance of students from VLE big data using deep learning models

Predicting academic performance of students from VLE big data using deep learning models

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

A Framework to Support Interdisciplinary Engagement with Learning Analytics

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