Active Learning in an Environment of Innovative Training and Sustainability. Mapping of the Conceptual Structure of Research Fronts through a Bibliometric Analysis

Abstract

The present study seeks to map and visualize up-to-date perspectives of the topic of active learning by analyzing and interpreting the different elements that make up learning ecosystems within the European Higher Education Area. With this aim, scientometric methods were employed to analyze a sample of 474 articles recovered from Web of Science (WoS) during the three-year period between 2018 and 2020. All articles examined the topic of active learning. Keywords (authors’ keywords and ‘keywords plus’) from the manuscripts were examined through co-occurrence analysis in order to establish the conceptual structure of active learning. Among the different trends and emerging topics identified, there is an important presence of topics related to technology applied to the field of education, where digital contexts acquire a preponderant role in current education. These innovative changes focused on the digital updating and exploitation of technology represent a methodological challenge that requires an involvement and commitment to this new space for educational practice by teachers and students.

Comprehensive Bibliometric Analysis of the Kynurenine Pathway in Mood Disorders: Focus on Gut Microbiota Research

“…The most relevant nodes belonged to the same clusters with the same colors, which represented close co-occurring relationships. The node size and link line width were proportional to the extent of co-occurrence and the strength of co-occurring relationships between nodes, respectively ( Rodríguez-Sabiote et al, 2020 ). Additionally, the betweenness centrality was used to measure the importance of nodes in the network.…”

“…Additionally, the betweenness centrality was used to measure the importance of nodes in the network. The more important the node, the higher the betweenness centrality, thus indicating that more information passed through the node ( Rodríguez-Sabiote et al, 2020 ). The hot topic “quinolinic acid,” which belonged to the red cluster, notably was the largest and had the highest calculated betweenness centrality value of 57.01.…”

“…Accordingly, the upper right quadrant (i.e., quadrant 1) pertains to motor themes that are both important and well-developed, the upper left quadrant (i.e., quadrant 2) is associated with highly developed and isolated themes, the lower left quadrant (i.e., quadrant 3) refers to emerging or declining themes, and the lower right quadrant (i.e., quadrant 4) contains transversal and basic themes ( Cobo et al, 2011 ). The clusters are represented by bubbles within the map, which are labeled by the keywords with the highest occurrences, and their sizes are proportional to the keyword occurrences ( Rodríguez-Sabiote et al, 2020 ). Notably, two clusters corresponding to “quinolinic acid” and “cerebrospinal-fluid,” composed of 43 and 44 keywords, respectively, were positioned in quadrant 3, which was characterized by both low centrality and low density.…”

Green Infrastructures and Grand Environmental Challenges: A Review of Research Trends by Keyword

“…For the development of this research, two software programs, VOSviewer and Biblioshiny, were used for bibliometric mapping. In the case of the latter, it takes into account the advantages of the calculation algorithms and the graphical representations generated [20]. The justification of choice was determined mainly because VOSviewer can analyze clusters, co-occurrence, and display trends in their entirety using a full counting method.…”

“…It uses the same file as the previous case and runs it through the R-studio console loading library (bibliometrix); biblioshiny. Once the action was executed, we deployed the "biblioshiny" interface as it is easier and more intuitive to use, thus avoiding the R language [20,26]. Following this step, we selected the database to obtain the results and view the visualization maps.…”