Características del Diseño de Estrategias de microaprendizaje en escenarios educativosrevisión sistemática

  1. Viviana Betancur-Chicué 1
  2. Ana García-Valcárcel Muñoz-Repiso 1
  1. 1 Universidad de Salamanca, España
Journal:
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Year of publication: 2023

Issue Title: Competencias y metodologías innovadoras para la educación digital

Volume: 26

Issue: 1

Pages: 201-222

Type: Article

DOI: 10.5944/RIED.26.1.34056 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: RIED: revista iberoamericana de educación a distancia

Abstract

Microlearning (ML) as a training strategy has been used in different areas due to the format it offers based on short-duration contents arranged in a learning path adaptable to each position, role, or particular need. Taking these characteristics into consideration, this paper identifies the main advantages and disadvantages of the use of ML, the design characteristics that prevail in the development of learning contents with this format, and the areas of knowledge in which it has had greatest impact. For this, literature was systematically reviewed using a protocol which covers research published between 2018 and 2021, identified through Scopus and Web of Science. The data collected was subject to a filtering process with a content analysis according to selection criteria and the research questions. It is concluded that some of the main advantages of the ML are: (i) it provides an agile strategy for professional training, (ii) it is useful in the development of introductory or basic topics, and (iii) it can reduce the cognitive load. The main disadvantage is its limited contribution to the development of more complex topics or skills, and the lack of peer interaction strategies. In respect of the studies analyzed, it was found that the areas of knowledge that have explored ML to a greater extent have been health, education, and engineering (computer science). Finally, regarding the design characteristics that prevail in the development of ML content, we highlight a structure based on introduction, microcontent, and questions, placing great importance on educational videos.

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