RT Journal Article
T1 Technologies for Data-Driven Interventions in Smart Learning Environments [Editorial]
A1 Hernández-Leo, Davinia
A1 Muñoz Merino, Pedro José
A1 Bote Lorenzo, Miguel L.
A1 Gasevic, Dragan
A1 Jarvela, Sanna
AB Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.
PB IEEE
SN 1939-1382
YR 2023
FD 2023-06-01
LK https://hdl.handle.net/10016/38990
UL https://hdl.handle.net/10016/38990
LA eng
NO This work was supported in part by the Spanish Ministry ofScience and Innovation through Smartlet and the H2OLearnProjects under Grant MICIN/AEI/10.13039/501100011033,and in part by the Fondo Europeo de Desarrollo Regional(FEDER) under Grant TIN2017-85179-C3-1-R, GrantTIN2017-85179-C3-2-R, Grant TIN2017-85179-C3-30R,Grant PID2020-112584RB-C31, Grant PID2020-112584RB C32, and Grant GPID2020-112584RB-C33. The work ofDavinia Hernández-Leo (Serra Húnter) was supported byICREA through the ICREA Academia Program.
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RD 1 sept. 2024