RT Journal Article
T1 Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review
A1 Tabuenca, Bernardo
A1 Serrano-Iglesias, Sergio
A1 Carruana Martín, Adrián
A1 Villa-Torrano, Cristina
A1 Dimitriadis Damoulis, Ioannis
A1 Asensio Perez, Juan Ignacio
A1 Alario-Hoyos, Carlos
A1 Gómez-Sánchez, Eduardo
A1 Bote Lorenzo, Miguel Luis
A1 Martinez-Mones, Alejandra
A1 Delgado Kloos, Carlos
AB Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies, such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community since different research works employ the adjective “smart” to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency and also to contribute to a common understanding of the SLE concept, this article presents a systematic literature review of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty-eight papers out of an initial list of 1341 papers were analyzed to identify the following: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this article presents a comprehensive characterization to describe SLEs through their affordances, the technologies used, and pedagogical approaches considered in the selected papers. As a result, specific core functions of SLEs are identified and explained. This work aims at ensuring a relevant knowledge base and reference toward the implementation of future SLEs.
PB IEEE
SN 1939-1382
YR 2021
FD 2021-04-01
LK https://hdl.handle.net/10016/33669
UL https://hdl.handle.net/10016/33669
LA eng
NO This work was supported in part by the European Regional Development Fund as well as by the National Research Agency of the Spanish Ministry of Science, Innovations, and Universities through the SmartLet project under Grant TIN2017-85179-C3-1-R and Grant TIN2017-85179-C3-2-R, in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is cofunded by the European Structural Funds (FSE and FEDER), in part by the European Regional Development Fund, as well as the Regional Council of Education of Castile, and Leon through CasualLearn project under Grant VA257P18, in part by the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), InnovaT (5898758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP), through the Erasmus+ Knowledge Alliances project ColMOOC (588438-EPP-1-2017-1-EL-EPPKA2-KA), and through the Erasmus+ Strategic Partnerships for higher education project TEASPILS (2020-1-ES01-KA203-082258).
DS e-Archivo
RD 21 jul. 2024