UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies
By Rashid Mehmood, F. Alam, Nasser N. Albogami, Iyad A. Katib, Aiiad Ahmad Albeshri, Saleh M. Altowaijri. 2017
137 citations recorded by the source corpus.
The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.
Subfields: Computer Networks and Communications, Computer Science Applications
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In Computer Networks and Communications
- Mohsen Guizani
- Ala Al-Fuqaha
- M. Mohammadi
- Sameh Sorour
- Billy Pik Lik Lau
- Aiiad Ahmad Albeshri
- Iyad A. Katib
- Chau Yuen
In Computer Science Applications
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[77] also used DNNs for a personalized ubiquitous e-teaching and”
- A survey of data fusion in smart city applications (2019, background): “A distance learning framework is proposed in [115], which enables personalized learning to cater what is best for each individual user.”
- UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, background): “See [21], for instance, for motivations of smart cities and societies.”