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      Comprensión lectora autorregulada apoyada en tecnología en estudiantes de Educación Básica Translated title: Compreensão de Leitura Autorregulada Apoiada pela Tecnologia em Alunos da Educação Básica Translated title: Self-Regulated reading comprehension supported by technology in basic education students

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          Abstract

          Resumen Introducción. El aprendizaje autorregulado permite mejorar las habilidades de autoevaluación y selección de tareas de manera eficaz. Sin embargo, no se sabe si la autorregulación apoyada en tecnología mejora el desempeño en la comprensión lectora. Objetivo. Explorar la efectividad del aprendizaje autorregulado en línea, con base en tareas de resolución de problemas utilizando un algoritmo de selección, aplicado a la lectura comprensiva. Metodología. Se llevó a cabo un estudio experimental en línea con 76 estudiantes distribuidos aleatoriamente en dos grupos: uno recibió capacitación con ejemplos modelados sobre cómo seleccionar las tareas de lectura basándose en el desempeño y esfuerzo mental de tareas previas (i.e., experimental), y el otro seleccionó las tareas según su preferencia (i.e., control). Resultados. El ANOVA de los datos de la fase de selección de tareas reveló que el grupo experimental no alcanzó un alto nivel de precisión en la selección de tareas y su desempeño fue bajo. Sin embargo, en la fase de prueba posterior, el grupo experimental logró un más alto nivel de desempeño comparado con el grupo control. Discusión. Se concluye que la comprensión lectora autorregulada en un entorno tecnológico puede mejorar los resultados de una prueba de comprensión cuando se guía la toma de decisiones con base en el desempeño y carga cognitiva previa. Se finaliza con recomendaciones para la investigación futura y la práctica educativa.

          Translated abstract

          Resumo Introdução. A aprendizagem autorregulada permite melhorar as habilidades de autoavaliação e seleção de tarefas. No entanto, não se sabe se a autorregulação apoiada pela tecnologia melhora o desempenho na compreensão de leitura. Objetivo. Explorou-se a eficácia da aprendizagem autorregulada online, com base em tarefas de resolução de problemas usando um algoritmo de seleção, aplicado à leitura compreensiva. Metodologia. Foi conduzido um estudo experimental online com 76 alunos distribuídos aleatoriamente em dois grupos. Um grupo recebeu treinamento com exemplos modelados sobre como selecionar tarefas de leitura com base no desempenho e esforço mental de tarefas anteriores (grupo experimental); enquanto o outro, selecionou tarefas de acordo com suas preferências (grupo de controle). Resultados. A análise de variância (ANOVA) dos dados da fase de seleção de tarefas revelou que o grupo experimental não alcançou um alto nível de precisão na seleção de tarefas e seu desempenho foi baixo. No entanto, na fase de teste posterior, o grupo experimental obteve um nível mais alto de desempenho em comparação com o grupo de controle. Discussão. Os resultados sugerem que a compreensão de leitura autorregulada em um ambiente tecnológico pode melhorar o desempenho em um teste de compreensão quando a tomada de decisões é orientada pelo desempenho e carga cognitiva anteriores. O estudo encerra-se com recomendações para pesquisas futuras e práticas educacionais.

          Translated abstract

          Abstract Introduction. Self-regulated learning enables the effective improvement of self-assessment skills and task-selection abilities. However, it is unknown whether technology-supported self-regulation enhances performance in reading comprehension. Objective. This research aimed to explore the effectiveness of online self-regulated learning, based on problem-solving tasks, using a selection algorithm applied to reading comprehension. Method. The research was an online experimental study conducted with 76 students. They were randomly distributed into two groups: one received training with modeled examples on how to select reading tasks based on the performance and mental effort of previous tasks (i.e., experimental); the other selected tasks based on their preference (i.e., control). Results. The ANOVA analysis of the task selection phase data revealed that the experimental group did not achieve a high level of accuracy in task selection, and their performance was low. However, in the subsequent testing phase, the experimental group achieved a higher performance level than the control group. Discussion. It is concluded that self-regulated reading comprehension in a technological environment can improve comprehension test results when decision-making is guided by previous performance and cognitive load. The study concludes with recommendations for future research and educational practice.

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          Cognitive Architecture and Instructional Design: 20 Years Later

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            Cognitive load theory in health professional education: design principles and strategies.

            Cognitive load theory aims to develop instructional design guidelines based on a model of human cognitive architecture. The architecture assumes a limited working memory and an unlimited long-term memory holding cognitive schemas; expertise exclusively comes from knowledge stored as schemas in long-term memory. Learning is described as the construction and automation of such schemas. Three types of cognitive load are distinguished: intrinsic load is a direct function of the complexity of the performed task and the expertise of the learner; extraneous load is a result of superfluous processes that do not directly contribute to learning, and germane load is caused by learning processes that deal with intrinsic cognitive load. This paper discusses design guidelines that will decrease extraneous load, manage intrinsic load and optimise germane load. Fifteen design guidelines are discussed. Extraneous load can be reduced by the use of goal-free tasks, worked examples and completion tasks, by integrating different sources of information, using multiple modalities, and by reducing redundancy. Intrinsic load can be managed by simple-to-complex ordering of learning tasks and working from low- to high-fidelity environments. Germane load can be optimised by increasing variability over tasks, applying contextual interference, and evoking self-explanation. The guidelines are also related to the expertise reversal effect, indicating that design guidelines for novice learners are different from guidelines for more experienced learners. Thus, well-designed instruction for novice learners is different from instruction for more experienced learners. Applications in health professional education and current research lines are discussed.
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              A power primer

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                Author and article information

                Journal
                ree
                Revista Electrónica Educare
                Educare
                Universidad Nacional. CIDE (Heredia, Heredia, Costa Rica )
                1409-4258
                1409-4258
                December 2023
                : 27
                : 3
                : 271-289
                Affiliations
                [1] Guayaquil orgnameUniversidad del Pacífico Ecuador nelly.benavides@ 123456upacifico.edu.ec
                [2] Guayaquil orgnameUniversidad del Pacífico Ecuador jimmy.zambrano@ 123456upacifico.edu.ec
                Article
                S1409-42582023000300271 S1409-4258(23)02700300271
                10.15359/ree.27-3.17221
                b715d615-399e-422d-877e-d0f8514d88bf

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International License.

                History
                : 03 July 2022
                : 29 November 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 39, Pages: 19
                Product

                SciELO Costa Rica

                Categories
                Artículo

                Aprendizaje,autoevaluación,rendimiento escolar,resolución de problemas,Aprendizagem,resolução de problemas,desempenho escolar,autoavaliação,Learning,self-assessment,school performance,problem resolution

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