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Title: On Learning in a Smart City Environment
Authors: Nikolov, Roumen
Shoikova, Elena
Krumova, Milena
Kovatcheva, Eugenia
Dimitrov, Velian
Chikalanov, Alexander
Keywords: Smart City
Smart Learning Environment
Full Context Awareness
Big Data and Learning Analytics
Autonomous Decision-Making
Learning Scenario
Forging Online Education Through FIRE
Issue Date: 2015
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Serdica Journal of Computing, Vol. 9, No 3-4, (2015), 223p-240p
Abstract: Advances in technology in recent years have changed the learning behaviours of learners and reshaped teaching methods and learning environments. This paper overviews a foundational framework and provides models for planning and implementing smart learning environments. Gartner’s 2015 Hype Cycle for Emerging Technologies identifies the computing innovations such as Internet of Things, Advanced Analytics, Machine Learning, Wearables, etc., that organisations should monitor. Learners and students, being the future drivers of these industries, are the main human resource to fulfil the vacancies of these work forces. Constant improvements and re-evaluation of the curriculum has to be done regularly to keep the learners up-to-date in meeting the requirements of these industries and corporations. Universities benefit from these thinking-outside-the-box practices by equipping students with work force experience that involves more hands-on tasks with real-life infrastructures. The introduction is focused on analysis of emerging industries and new types of jobs that require future personnel to be well equipped to meet the expansion requirements of these industries and keep up with their development needs. Section 2 looks at the future Internet domain landscape that comprises a great diversity of technology related topics involved in the implementation of Smart Learning Environments. The purpose of section 3 is to overview a foundational framework and major considerations for the planning and implementation of smart learning environments, behind which is the convergence of advances and developments in social constructivism, psychology, and technology. Section 4 introduces the smart learning models which are developed to reflect the dynamic knowledge conversion processes in technology enabled smart learning environments. The last section presents a case study of a learning scenario entitled “Monitoring the environmental parameters in a Smart City” as an illustration of experimental learning on Internet of Things, which proofs the power of the FORGE (Forging Online Education through FIRE) FP7 project methodology and infrastructure for building remote labs and delivering them to students. ACM Computing Classification System (1998): K.3.2.
ISSN: 1312-6555
Appears in Collections:Volume 9 Number 3-4

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