On Learning in a Smart City Environment

• Published in: Serdica : journal of computing Vol. 9; no. 3-4; pp. 223 - 240
• Main Authors: Nikolov, Roumen, Shoikova, Elena, Krumova, Milena, Kovatcheva, Eugenia, Dimitrov, Velian, Chikalanov, Alexander
• Format: Journal Article
• Language: English
• Published: 12.12.2016
• ISSN: 1312-6555; 1314-7897
• DOI: 10.55630/sjc.2015.9.223-240

Advances in technology in recent years have changed the learningbehaviours of learners and reshaped teaching methods and learningenvironments. This paper overviews a foundational framework and providesmodels for planning and implementing smart learning environments. Gartner’s 2015 Hype Cycle for Emerging Technologies identifies the computinginnovations 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 tofulfil the vacancies of these work forces. Constant improvements and re-evaluationof the curriculum has to be done regularly to keep the learners up-to-date inmeeting the requirements of these industries and corporations. Universities benefit from these thinking-outside-the-box practices by equippingstudents 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 futurepersonnel to be well equipped to meet the expansion requirements of theseindustries and keep up with their development needs. Section 2 looks atthe future Internet domain landscape that comprises a great diversity oftechnology 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 technologyenabled 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 infrastructurefor building remote labs and delivering them to students.ACM Computing Classification System (1998): K.3.2.