Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Abstract

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 paper presents a systematic literature review (SLR) of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty eight papers out of an initial list of 1,341 papers were analyzed to identify: 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 paper presents a comprehensive characterization Manuscript sent for review July 30, 2020. Revised month day, year; Accepted month day, year. Date of publication month day, year.

Demonstration of SCARLETT: A Smart Learning Environment to Support Learners Across Formal and Informal Contexts

“…In recent years, the growth and diversity of learning systems and tools has fostered the appearance of Smart Learning Environments (SLEs). SLEs aim to provide personalized support to learners taking into account both their individual learning needs and context [8]. SLEs make a combined use of Virtual Learning Environments (VLEs), mobile devices and applications, and Internet of Things (IoT) devices so as to gather data about the interactions of learners and their current learning contexts across different physical and virtual spaces.…”

“…Learners can reflect on concepts discussed in formal education in appealing informal learning activities, while informal learning experiences can be extended and complemented with formal lectures for the provision of appropriate feedback that can help to settle the reflections. However, a limited number of contributions have explored this connection in SLEs [8], specially due to the combination of multiple learning environments from both formal and informal contexts supporting learning activities that happen in different physical and virtual spaces. This demonstration shows how SCAR-LETT addresses them in order to provide informal learning support to learners connected with the formal learning situation.…”

A review on data fusion in multimodal learning analytics and educational data mining

“…Learners complete some components online and do others in person (Sánchez Ruiz et al, 2021). Smart learning environments (SLEs) are physical environments enriched with digital, context‐aware, adaptive devices which aim to achieve more effective, better‐quality learning (X. Chen et al, 2021; Tabuenca et al, 2021). SLEs contain multiple sources of data which, combined together, can offer a better understanding of the educational process. All these new type of learning environments produce a huge amount of student's data interaction.…”

“…• Smart learning environments (SLEs) are physical environments enriched with digital, context-aware, adaptive devices which aim to achieve more effective, better-quality learning (X. Chen et al, 2021;Tabuenca et al, 2021). SLEs contain multiple sources of data which, combined together, can offer a better understanding of the educational process.…”

Smart Sensors for Augmented Electrical Experiments

“…In particular, we consider the various integrated sensor systems which, in addition to the seamless integration of the virtual elements into the physical environment, also enable the creation of an accurate digital twin. Following the core functions of smart learning environments identified by Tabuenca et al [ 30 ], we focus on three facets in particular: Sense: Our system is capable of collecting information from the context in which it is introduced; Analyze: Our system is able to generate higher-level indicators from the data collected in the sensing process using data analysis techniques; React: While AR has typically been employed without sensor input or cumbersome, separate sensors, our system enables opportunities for new and expanded experiments through integrated sensors, such as educational data mining analysis and AI-based, individualized feedback using the same sensor technology. In this way, our system will prospectively provide customized recommendations for stakeholders based on the data collected during the sensing process and its interpretation performed during the analysis process.…”