Bringing tabletop technologies to kindergarten children

Taking computer technology away from the desktop and into a more physical, manipulative space, is known that provide many benefits and is generally considered to result in a system that is easier to learn and more natural to use. This paper describes a design solution that allows kindergarten children to take the benefits of the new pedagogical possibilities that tangible interaction and tabletop technologies offer for manipulative learning. After analysis of children's cognitive and psychomotor skills, we have designed and tuned a prototype game that is suitable for children aged 3 to 4 years old. Our prototype uniquely combines low cost tangible interaction and tabletop technology with tutored learning. The design has been based on the observation of children using the technology, letting them freely play with the application during three play sessions. These observational sessions informed the design decisions for the game whilst also confirming the children's enjoyment of the prototype

Applying data mining to discover common learning routes in Moodle

En este artículo, aplicamos técnicas de minería de datos para descubrir rutas de aprendizaje frecuentes. Hemos utilizado datos de 84 estudiantes universitarios, seguidos en un curso online usando Moodle 2.0. Proponemos agrupar a los estudiantes, en primer lugar, a partir de los datos de una síntesis de uso de Moodle y/o las calificaciones finales de los alumnos en un curso. Luego, usamos los datos de los logs de Moodle sobre cada cluster/grupo de estudiantes separadamente con el fin de poder obtener más específicos y precisos modelos de procesos del comportamiento de los estudiantes.In this paper, we apply techniques data mining to discover common learning routes. We have used data from 84 undergraduate college students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle’s usage summary and/or the students’ final marks in the course. Then, we use data from Moodle’s logs about each cluster/group of students separately in order to be able to obtain more specific and accurate process models of students’ behaviour

Shoulder pain prevalence in competitive swimmers: A pilot study

Objetivos: conocer la prevalencia de dolor de hombro en nadadores de competición, sus características y su relación con factores antropométricos y deportivos. Métodos: estudio de prevalencia. Ciento cuarenta nadadores/as entre 12 y 24 años cumplimentaron un cuestionario durante una fase de entrenamiento específico. Resultados: el 25,7% revelaron padecer dolor de hombro. Se hallaron relaciones estadísticamente significativas entre el dolor de hombro y episodios previos de dolor (p<0,001), experiencia superior a tres años (p=0,014), Índice de Masa Corporal (p=0,015) y la especialidad estilo (p=0,008) y distancia (p=0,011). El dolor fue significativamente más intenso durante la actividad que en reposo (p<0,001). Conclusiones: el dolor de hombro en nadadores de competición entre 12 y 24 años parece ser un problema frecuente mayor Índice de Masa Corporal y a la especialidad del nadadorObjectives: to investigate the prevalence of shoulder pain in competitive swimmers and find out the characteristics of pain as well as its relation to anthropometric and sports factors. Methods: prevalence study. A hundred and forty competitive swimmers between 12 and 24 years old completed a questionnaire in a high intensity training phase. Results: 25.7% swimmers reported shoulder pain. There were significant statistical correlations between shoulder pain and previous episodes of pain (p<0.001), more than three years of experience (p=0.014), Body Mass Index (p=0.015) and stroke (p=0.008) and distance (p=0.011) specialty. Pain was statistically correlated with activity (p<0.001). Conclusions: shoulder pain seems to be a frequent and repetitive problem in competitive swimmers between 12 and 24 years old, which increases with years of practice. Furthermore, it seems to be associated with the activity, a higher Body Mass Index and the swimmers’ specialty

Effects of ADHD on writing composition product and process in school-age students

Objective: This study examined the relationship between ADHD and writing performance. Method: Students in Grades 3 to 7, 84 with ADHD and 135 age and gender-matched controls completed a writing task (including process logs), and measures of, working memory and attention. Results: Students with ADHD wrote texts of similar length but with poorer structure, coherence and ideation. 6.7% of the variance in writing quality was explained by whether or not the student had an ADHD diagnosis, after control for IQ and age-within-year, with students with ADHD producing text that was less coherent, well structured, and ideationally rich and to spend less time thinking about and reviewing their text. Half of the effect on text quality could be attributed to working memory and sustained attention effects. Conclusions: ADHD has some effect on writing performance which can, in part, be explained by working memory and attentional deficits

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area