José-Manuel Sáez-López

This study examined how educational robotics and machine learning activities contributed to the understanding of artificial intelligence (AI) among elementary school students, pre-service teachers, and practicing teachers. The intervention involved 1207 participants, including 1009 fifth-grade students, 93 practicing elementary school teachers, and 105 pre-service teachers. Participants engaged in a series of structured activities focused on educational robotics, visual block-based programming, and machine learning concepts. A pre-experimental design was employed to assess changes in understanding and to explore participants’ perceptions of the learning experience. Statistical analyses included Student’s t-tests, the Kruskal–Wallis H test, and Bonferroni-corrected post hoc comparisons. Results indicated pre–post gains in elementary school students’ understanding of machine learning, AI modeling processes, and fundamental computational concepts following participation in the intervention. Across the three groups, participants reported positive perceptions of the activities and recognized their value for learning about AI. Elementary school students reported higher motivation and stronger engagement in coding-related tasks, likely reflecting their previous experience with Scratch in school settings. Practicing teachers expressed a more interdisciplinary perspective on the educational applications of AI, whereas pre-service teachers demonstrated stronger conceptual understanding of the underlying principles. Overall, the findings suggest that educational robotics and machine learning activities are associated with positive learning experiences and observed gains in AI-related understanding. However, given the pre-experimental design without a control group or random assignment, the results should be interpreted as evidence of observed changes and participant perceptions rather than demonstrated effectiveness.

This research examines the implementation of machine learning concepts in elementary education through a sample of 1,009 fifth-grade students. The intervention involved a set of structured activities related to machine learning using Teachable Machine and RAISE (built on Scratch 3.0). A pre-experimental research design was employed, combining descriptive analysis with statistical inference. Specifically, a Student’s t-test was applied to analyze the first dimension, while the Wilcoxon test was used for the second dimension. The results indicate that elementary school students improved their understanding of machine learning and the ways in which artificial intelligence models are developed. Furthermore, students with prior experience using Scratch in school obtained higher scores and reported greater motivation compared to those without experience in block-based programming environments. The findings suggest that interactive learning activities focused on machine learning are effective for motivating students and facilitating their comprehension of AI, including how it is trained and generated. Additionally, these activities increased engagement and enjoyment during the sessions. Overall, the study demonstrates that implementing pedagogical designs aimed at introducing machine learning and artificial intelligence in primary education is both feasible and beneficial.

La competencia digital es una temática recurrente en la investigación educativa, especialmente en los últimos años, debido a los avances científicos y sociales, así como a los cambios en la normativa educativa y en los procesos de capacitación docente en este ámbito. No obstante, la cantidad de estudios centrados en la competencia digital en el alumnado de educación primaria es limitada. En consecuencia, este estudio se centra en evaluar la percepción del alumnado del tercer ciclo de Educación Primaria sobre su competencia digital. Se ha desarrollado un cuestionario basado en los descriptores operativos de la competencia digital para esta etapa, compuesto por 17 ítems que recogen los principales aprendizajes competenciales. Por ello, se ha administrado a una muestra de 450 estudiantes del tercer ciclo de Educación Primaria, previa a la realización de un análisis de fiabilidad (alfa de Cronbach) y de contenido (juicio de expertos). Los resultados del estudio muestran una autopercepción positiva de la competencia digital por parte del alumnado, en contraste con los bajos resultados de integración efectiva de las TIC en el entorno educativo. Como conclusión, es importante considerar esta discrepancia en los resultados, para llevar a cabo estrategias de enseñanza y aprendizaje que promuevan una integración tecnológica en el aula, con el objetivo de mejorar tanto la competencia digital como el rendimiento académico del alumnado, ya que se evidencia un uso e integración de las TIC en la cotidianidad del alumnado que inciden directamente en el ámbito educativo.

This study evaluates the results of dynamic and innovative educational robotics activities in elementary schools, with 704 students in grades 4, 5, and 6 across nine schools in the City of Madrid. In 19 classrooms, students worked on projects using block-based coding and sensor-based educational robotics, developing their understanding of computational concepts and engaging in interdisciplinary and environmental educational tasks. The computational robotics test was used, applying a paired-samples t-test for the first dimension and a Wilcoxon signed-rank test for the second. In the post-test, students demonstrated improved understanding of computational concepts, particularly sequences, loops, sensors, and actuators. They learned how to assemble a robot, and they positively evaluated environmental and science projects. Other curricular areas showed lower scores and no significant improvements. The students felt that working with robotics made them more active and the class more engaging. In short, active implementation of dynamic, interdisciplinary, and environmental activities in elementary education is recommended. Manipulation, coding, and creativity in projects foster active learning of the content with enthusiasm and commitment.

Programming and robotics-based training programs have been shown to enhance computational thinking and self-efficacy, yet limited attention has been paid to preparing future teachers to effectively implement these methodologies in educational practice. This study analyses the impact of an educational intervention based on visual block programming, educational robotics, and machine learning on the initial training of pre-service teachers and in-service primary school teachers. A quasi-experimental design was employed. The sample consisted of 170 participants: 83 students enrolled in a Primary Education degree program and 87 in-service primary school teachers. The instructional procedure was implemented through hands-on activities with micro:bit, Maqueen robots, and introductory machine learning concepts. Data were collected using a coding, robotics, and machine learning knowledge test, along with several validated Likert-type scales to assess attitudes toward the curricular integration of these technologies. For the first dimension, Student’s t -tests and linear regression analyses were conducted, while correlation analyses and nonparametric tests were applied to the second dimension. The findings revealed significant improvements in the acquisition of basic computational concepts (sequences, loops, and conditionals) and in the understanding of machine learning, with university students outperforming practicing teachers. Comparative tests indicated a greater self-perception of technological competence among university students, particularly in block-based programming and the use of game engines for educational purposes. The results suggest that the structured integration of robotics and machine learning appears to constitute a viable and effective strategy for enhancing teacher training, promoting active methodologies, and fostering an interdisciplinary approach in primary education.

This study evaluated the integration of coding, visual block programming, game engines, and machine learning (ML) modeling in initial teacher education. The aim was to examine the feasibility of this instructional approach and its association with pre-service teachers’ computational knowledge and attitudes toward programming and artificial intelligence (AI). A one-group pretest-posttest design was implemented with 129 undergraduate students enrolled in Primary Education degree programs at two public universities in Spain. Knowledge of computational concepts was assessed with a 10-item test analyzed using a paired-samples Student’s t-test, while attitudes toward programming and ML were evaluated with two Likert scales analyzed using the Wilcoxon signed-rank test. Significant pre-post improvements were observed in participants’ understanding of computational concepts and in several attitude measures related to programming, coding, and ML. Participants also reported positive perceptions of the educational value of coding and AI. However, no statistically significant improvements were found in self-reported practical experience with visual block programming or educational game engine programming. These findings suggest that integrating coding, game engines, and ML into initial teacher education is associated with positive learning outcomes and favorable attitudes toward these technologies. Nevertheless, given the one-group pretest-posttest design, the observed improvements should be interpreted cautiously, as causal relationships cannot be established. The results also indicate that longer and more intensive practical experiences may be necessary to develop technical programming competencies. Overall, this instructional proposal may serve as a useful framework for future teacher education initiatives aimed at promoting computational thinking and AI literacy.

Este trabajo hace un recorrido por los requerimientos que debe cumplir una universidad antes de ofertar educación virtual, iniciando con el marco legal que garantice el funcionamiento adecuado de esta modalidad, este marco legal debe contemplar a los estudiantes como el eje de la educación, los profesores como el catalizador del proceso de aprendizaje, la plataforma con la tecnología como el medio de comunicación y colaboración y, por último, la inteligencia artificial como una herramienta fundamental. Posterior a ello, contar con los requisitos del modelo educativo, diseño instruccional, estudiantes, profesores, plataforma educativa y la inteligencia artificial. Una vez iniciada la modalidad virtual se deben atender aspectos como personalizar el aprendizaje, gestionar contenidos y herramientas, facilitar la comunicación y la colaboración, garantizar el soporte y la accesibilidad, análisis y seguimiento académico de los estudiantes, y finalmente, la actualización y la mejora continua. Este trabajo no pretende ser una guía detallada para la implementación de la modalidad virtual, aspira a dar una idea a directivos y funcionarios que desean implementar modalidad virtual para posteriormente reunir a un equipo multidisciplinario integrado por pedagogos, abogados, tecnólogos y otros que desarrollen una planeación que garantice una modalidad virtual exitosa.

Se analizan las diferencias entre ser un estudiante en modalidad presencial y ser un estudiante en modalidad virtual. Para la búsqueda de las fuentes se utilizó el método PRISMA el cual garantiza la rigurosidad en la revisión sistemática de la literatura. Se indican las once principales competencias que debe poseer un estudiante universitario para tener éxito en la modalidad virtual, las cuales son autodisciplina, gestión del tiempo, competencias digitales, comunicación escrita efectiva, colaboración a distancia, autoevaluación, adaptabilidad, iniciativa, habilidad para la autorregulación emocional, pensamiento crítico y autonomía académica. Como conclusión, existen grandes diferencias entre ser un estudiante en modalidad presencial y en modalidad virtual, los segundos enfrentan desafíos que no son tan complejos para un estudiante en modalidad presencial. Los estudiantes que opten por la modalidad virtual deben poseer al menos las once principales competencias para tener mayores posibilidades de éxito académico.