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.

Research and development often move forward based on buzzwords. New terms are coined to summarize new developments, often with several interpretations and without a formal definition. The term Smart Education has been coined to represent a move forward in technology-enhanced education, but what is behind it? Does it represent something essentially different from the educational technologies used before? In this paper, we do a systematic literature review to understand how this term is used, what the technologies behind it are, and what promises are made. We conclude that although the term is fuzzy, there are indeed several developments available today that can make educational technologies much more adapted to the learner and therefore underpin the learning in a smarter way.

COVID-19 has brought new hybrid learning environments with some students in the classroom and some others online, synchronously, due to the needs of social distancing. These new hybrid learning environments pose new challenges, for example for group collaboration. This paper presents Smart Groups, a system aimed at helping teachers to orchestrate collaboration in hybrid learning environments and assesses its usability and usefulness through a simulation study. Smart Groups identifies the students that are in the classroom and online, automates the creation of groups (recommending collaborative learning flow patterns to teachers and considering the previous work done by students), supports the communication among students and the use of additional tools and resources for collaboration, and helps maintain the safety distance among the students who are in the classroom. The usability of Smart Groups has been assessed through a mock-up by 60 users (41 students and 19 teachers) with the system usability scale (SUS) obtaining good results (mean = 75.47, standard deviation = 14.95, median = 76.25). A subgroup (10 teachers out of 19) carried out follow-up interviews using the technology acceptance model (TAM) and highlighted the usefulness of Smart Groups to orchestrate collaboration in hybrid learning environments. Implications for practice or policy: Smart Groups supports teachers in the orchestration of groups in hybrid learning environments. Smart Groups facilitates group coordination and communication among students. Smart Groups helps maintain the safety distance.

BackgroundBiomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature.ObjectiveThe aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE.MethodsOur approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms.ResultsOur experiments show promising results with an F1 of 69% on the test dataset.ConclusionsTo the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

BACKGROUND Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. RESULTS Our experiments show promising results with an F1 of 69% on the test dataset. CONCLUSIONS To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.