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Enhancing Feedback Uptake and Self-Regulated Learning in Procedural Skills Training: Design and Evaluation of a Learning Analytics Dashboard
Indexado
WoS WOS:001295934400009
Scopus SCOPUS_ID:85202546173
DOI 10.18608/JLA.2024.8195
Año 2024
Tipo artículo de investigación

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



Remote technology has been widely incorporated into health professions education. For procedural skills training, effective feedback and reflection processes are required. Consequently, supporting a self-regulated learning (SRL) approach with learning analytics dashboards (LADs) has proven beneficial in online environments. Despite the potential of LADs, understanding their design to enhance SRL and provide useful feedback remains a significant challenge. Focusing on LAD design, implementation, and evaluation, the study followed a mixed-methods twophase design-based research approach. The study used a triangulation methodology of qualitative interviews and SRL and sensemaking questionnaires to comprehensively understand the LAD's effectiveness and student SRL and feedback uptake strategies during remote procedural skills training. Initial findings revealed the value students placed on performance visualization and peer comparison despite some challenges in LAD design and usability. The study also identified the prominent adoption of SRL strategies such as help-seeking, elaboration, and strategic planning. Sensemaking results showed the value of personalized performance metrics and planning resources in the LAD and recommendations to improve reflection and feedback uptake. Subsequent findings suggested that SRL levels significantly predicted the levels of sensemaking. The students valued the LAD as a tool for supporting feedback uptake and strategic planning, demonstrating the potential for enhancing procedural skills learning.

Revista



Revista ISSN
1929-7750

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Disciplinas de Investigación



WOS
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Scopus
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SciELO
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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Villagran, Ignacio Hombre Pontificia Universidad Católica de Chile - Chile
2 Hernandez, Rocio - Pontificia Universidad Católica de Chile - Chile
3 Schuit, Gregory Hombre Pontificia Universidad Católica de Chile - Chile
4 Neyem, Andres Hombre Pontificia Universidad Católica de Chile - Chile
5 Fuentes, Javiera Mujer Pontificia Universidad Católica de Chile - Chile
Maastricht Univ - Países Bajos
6 Larrondo, Loreto - Pontificia Universidad Católica de Chile - Chile
7 Margozzini, Elisa - Pontificia Universidad Católica de Chile - Chile
8 Hurtado, Maria T. - Pontificia Universidad Católica de Chile - Chile
9 Iriarte, Zoe - Pontificia Universidad Católica de Chile - Chile
10 Miranda, Constanza Mujer Johns Hopkins Univ - Estados Unidos
Pontificia Universidad Católica de Chile - Chile
11 Varas, Julian Hombre Pontificia Universidad Católica de Chile - Chile
12 Hilliger, Isabel Mujer Pontificia Universidad Católica de Chile - Chile

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Financiamiento



Fuente
Agencia Nacional de Investigación y Desarrollo
National Agency for Research and Development (ANID) under the Scholarship Program DOCTORADO BECAS CHILE/2020
Agenția Națională pentru Cercetare și Dezvoltare
CENIA
National Center for Artificial Intelligence
National Center for Artificial Intelligence (CENIA)
Department of Health Sciences Pontificia Universidad Catolica de Chile

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Agradecimientos



Agradecimiento
The Department of Health Sciences Pontificia Universidad Catolica de Chile funded this research. Also, the authors would like to acknowledge the National Agency for Research and Development (ANID) under the Scholarship Program DOCTORADO BECAS CHILE/2020 - 21202032 and the National Center for Artificial Intelligence (CENIA) , FB210017, BASAL.
The Department of Health Sciences Pontificia Universidad Cat\u00F3lica de Chile funded this research. Also, the authors would like to acknowledge the National Agency for Research and Development (ANID) under the Scholarship Program DOCTORADO BECAS CHILE/2020 \u2013 21202032 and the National Center for Artificial Intelligence (CENIA), FB210017, BASAL.

Muestra la fuente de financiamiento declarada en la publicación.