Browsing by Author "Hernández Román, Rocío Belén"

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    Enhancing Feedback Uptake and Self-Regulated Learning in Procedural Skills Training
    (2024) Villagrán Gutiérrez, Ignacio Andrés; Hernández Román, Rocío Belén; Schuit Condell, Gregory Kees; Neyem, Hugo Andrés; Fuentes Cimma, Javiera Carolina; Larrondo Vergara, María Loreto; Margozzini Delorenzo, Elisa; Hurtado Bunster, María Teresa; Iriarte Vásquez, Zoe; Miranda Mendoza, Constanza Sofía; Varas Cohen, Julián Emanuel; Hilliger Carrasco, Isabel
    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 two-phase 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.
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    Evaluating the Feedback Uptake for Developing Procedural Skills Remotely and Asynchronously: A Quantitative Study in Health Professions Training
    (John Wiley & Sons, 2025) Villagrán Gutiérrez, Ignacio Andrés; Hernández Román, Rocío Belén; Fuentes Cimma, Javiera Carolina; Miranda, Constanza; Varas Cohen, Julián; Neyem, Andrés; Sepúlveda Ramírez, María Gabriela del Rosario; Catril Yañez, Bárbara Lorena; García Valdés, Patricio Hernán; Hilliger, Isabel; Fuentes López, Eduardo
    Background Before they graduate, higher education students must demonstrate technical skills in different procedures, where health degrees such as physiotherapy stand out for their practical nature. Procedural skill development requires students to uptake feedback continuously, which is costly in terms of time and effort. Although the incorporation of technology has proven to support feedback delivery, further research is still needed to understand how these types of technologies can support feedback uptake and procedural skill attainment. Objectives This paper aims to examine the experience of undergraduate physiotherapy trainees with a focus on feedback uptake during a 4-session procedural training with a feedback-oriented platform. Methods We used process mining to study the trainees' behavioural patterns regarding feedback, besides conducting descriptive and multilevel analyses to evaluate the incorporation of feedback and its influence on trainees' performance. Results and Conclusions We observed that most trainees reviewed and incorporated the feedback provided throughout the training, particularly towards the final session. And those trainees who better incorporated the feedback exhibited better performance. Thus, the results of this study support the idea that remote and asynchronous training with a feedback-oriented platform can support feedback uptake to improve procedural skill attainment throughout a learning process.
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    Inovações no treinamento cirúrgico: explorando o papel da inteligência artificial e dos grandes modelos de linguagem (LLM)
    (2023) Varas Cohen, Julián Emanuel; Valencia Coronel, Brandon; Villagrán Gutiérrez, Ignacio Andrés; Escalona Vivas, Gabriel Enrique; Hernández Román, Rocío Belén; Schuit Condell, Gregory Kees; Duran Espinoza, Valentina Alexandra; Lagos Villaseca, Antonia Elisa; Jarry Trujillo, Cristián Ignacio; Neyem, Hugo Andrés; Achurra Tirado, Pablo Andrés
    O cenário do treinamento cirúrgico está evoluindo rapidamente com o surgimento da inteligência artificial (IA) e sua integração na educação e simulação. Este artigo explora as aplicações e benefícios potenciais do treinamento cirúrgico assistido por IA, em particular o uso de modelos de linguagem avançados (MLAs), para aprimorar a comunicação, personalizar o feedback e promover o desenvolvimento de habilidades. Discutimos os avanços no treinamento baseado em simulação, ferramentas de avaliação impulsionadas por IA, sistemas de avaliação baseados em vídeo, plataformas de realidade virtual (RV) e realidade aumentada (RA), e o papel potencial dos MLAs na transcrição, tradução e resumo do feedback. Apesar das oportunidades promissoras apresentadas pela integração da IA, vários desafios devem ser abordados, incluindo precisão e confiabilidade, preocupações éticas e de privacidade, viés nos modelos de IA, integração com os sistemas de treinamento existentes, e treinamento e adoção de ferramentas assistidas por IA. Ao abordar proativamente esses desafios e aproveitar o potencial da IA, o futuro do treinamento cirúrgico pode ser remodelado para proporcionar uma experiência de aprendizado mais abrangente, segura e eficaz para os aprendizes, resultando em melhores resultados para os pacientes.