ABSTRACT. We rely on Kinshuk et al. (2016) to prove that with latest developments in learning technologies, added to the manners information and communication technology are carried out, numerous stimulating likelihoods have arisen to transubstantiate students’ learning conducts and teachers’ pedagogical methods, but an alteration is required to essentially reconstruct the present learning settings in the direction of smart learning environments. The latter allow the synthesis of technology and pedagogy to generate an environment that comprises dynamic involvement of stakeholders in the students’ learning mechanism. We develop primary empirical research for our principal case study that determines that smart learning environments involve and put together formal and informal education with the purpose of constituting self-governing flexible learning settings for assisting individual students with actual and harmonious pedagogical experiences in omnipresent environments that frequently employ big data and education analytics techniques to integrate the mixture of actual information and the historical datasets with the aim of determining contextually significant learning patterns. Developing this case and making use of latest contributions to the literature, we consequently reflect on the aspect that smart learning environments further just-in-time education because they may furnish different degrees of adjustment and accuracy of various education circumstances for the students. Full context awareness facilitates such environments to supply students with reliable education circumstances and harmonious pedagogical experiences to combine a diversity of characteristics in the e-learning settings. Smart learning environments may enable smart learning furtherance that is contingent on each separate student’s education profile. Our findings support our theoretical discussion and empirical analysis and are consistent with research highlighting that via big data and learning analytics, such environments can determine cutting-edge and more successful education patterns by inspecting the data pools of diverse students and besides obtain constructive learning models, to supply advice to the students across prolonged periods of time. The self-governing knowledge management proficiency of smart learning environments allows them to routinely compile separate students’ learning profiles.
JEL codes: I23; D83

Keywords: techno-pedagogy knowledge; smart learning environments; learning mechanism; education analytics techniques; education profile

How to cite: Nica, Elvira (2017), “Techno-Pedagogy Knowledge in Smart Learning Environments,” Economics, Management, and Financial Markets 12(1): 75–81.

Received 2 April 2016 • Received in revised form 18 May 2016
Accepted 19 May 2016 • Available online 18 June 2016


This email address is being protected from spambots. You need JavaScript enabled to view it.
Center for Human Resources and Labor Studies
at AAER, New York;
Bucharest University of Economic Studies

Home | About Us | Events | Our Team | Contributors | Peer Reviewers | Editing Services | Books | Contact | Online Access

© 2009 Addleton Academic Publishers. All Rights Reserved.

Joomla templates by Joomlashine