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Health management students’ self-regulation and digital concept mapping in online learning environments

BACKGROUND: Self-regulation of learning is considered one of the key capabilities deemed essential for the healthcare system and its workers to cope successfully with the current challenges they are facing. Therefore, healthcare curricula are increasingly called upon to support self-regulation as a...

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Detalles Bibliográficos
Autores principales: Alt, Dorit, Naamati-Schneider, Lior
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891141/
https://www.ncbi.nlm.nih.gov/pubmed/33596899
http://dx.doi.org/10.1186/s12909-021-02542-w
Descripción
Sumario:BACKGROUND: Self-regulation of learning is considered one of the key capabilities deemed essential for the healthcare system and its workers to cope successfully with the current challenges they are facing. Therefore, healthcare curricula are increasingly called upon to support self-regulation as a central learning outcome. With scant relevant publications describing how students of medicine and other healthcare professions regulate their learning, this study set out to design and assess a problem-based learning using digital concept mapping in an online course and to evaluate the set of connections between this intervention and Health Management students’ self-regulation of learning. METHOD: Students of a Management of Health Service Organizations program (100) were presented with an ill-structured problem, relevant to their course content (accreditation process within hospitals) and were asked to argue for or against the implementation of the accreditation process. The participants were asked to detail five arguments to establish their decision by using Mindomo, a popular digital platform for designing concept maps. The students were given predefined criteria that allowed them to self-assess their maps. Data for the analysis were gathered by two measurements: Concept mapping for problem-based learning scale and the Online self-regulated learning scale and were analyzed by using Partial Least Squares - Structural Equation Modeling. RESULTS: The analyses showed that at the beginning of the process, students’ online self-regulation was found lower than at the end of the intervention, and only two self-regulation sub-factors: Goal setting and Task strategies, were positively linked to students’ perceptions of the intervention. After the intervention, the analyses showed that it increased the levels of four Online self-regulation sub-factors: Goal setting, Task strategies, Environment structuring, and Time management. CONCLUSIONS: Teachers need to recognize and account for different types of learners and encourage and scaffold students’ effective use of self-regulation strategies. Low self-regulated learners might fail to see the advantages of concept mapping in problem-solving activities. Combining these teaching and learning tools together with the use of advanced technology in an online course that encourages active learning enables the development and acquisition of abilities of self-directed learning among students in the medical and health management professions.