Percepción del “Clima Educativo” en los Estudios de Odontología en España mediante el Cuestionario Dundee Ready Environment Measure: una Perspectiva Multicéntrica

El proceso de Bolonia ha supuesto un punto de inflexión para la puesta en marcha de un proceso de desarrollo y potenciación de la Educación Superior Europea. Esta situación justificó la necesidad de un cambio del modelo docente o educativo, por lo que debemos centrarnos en las consecuencias que para la enseñanza universitaria tiene esta nueva realidad. Durante este periodo de “transición curricular”, el estudio del Clima Educativo proporciona información útil para todos los integrantes del proceso educativo. La existencia de un Clima Educativo positivo es el objetivo final de cualquier institución educativa, ya que garantiza una experiencia de enseñanza-aprendizaje exitosa. Debido a la escasa evidencia científica sobre esta temática desarrollada en nuestro país, en la presente Tesis Doctoral se analizó, aplicando la escala más utilizada en la literatura internacional (Dundee Ready Education Environment Measure, DREEM), cuál es la percepción del Clima Educativo de los principales protagonistas implicados en el proceso de enseñanza-aprendizaje en las facultades de Odontología. Tras el análisis de los resultados, se obtuvieron las siguientes conclusiones: 1. La versión española del cuestionario Dundee Ready Education Enviroment Measure (DREEM) es un instrumento confiable y válido y su estructura factorial se encuentra confirmada por los datos obtenidos. 2. La versión española del cuestionario Dundee Ready Education Enviroment Measure (DREEM) es un instrumento confiable para analizar el Clima Educativo de los estudiantes de Odontología desde la perspectiva de los profesores. Sin embargo, la estructura factorial de la escala adaptada al profesorado precisa ser revisada en una muestra más amplia, con la finalidad de incrementar la confiabilidad y validez de dicha versión. 3. El Clima Educativo de los estudiantes de Odontología de las facultades públicas españolas en un “periodo de transición curricular” es “más positivo que negativo”. Sin embargo, los estudiantes identificaron la existencia de algunos aspectos educativos problemáticos, asociados al desarrollo de un currículum tradicional. 4. El Clima Educativo es percibido de forma más positiva por parte de los estudiantes de Facultades de Odontología de grado único, por los mayores de 25 años, que generalmente tienen una formación académica previa, y por aquellos que cursan su último año de formación. 5. El Clima Educativo de los estudiantes de Odontología de las facultades públicas españolas en un “periodo de transición curricular” es percibido por los profesores como “más positivo que negativo”, con la existencia de pocos aspectos educativos problemáticos. 6. El Clima Educativo es percibido de forma más positiva por parte de los profesores con mayor edad y experiencia docente. 7. Aunque ambos colectivos, alumnado y profesorado, consideraron que el Clima Educativo es “más positivo que negativo”, los docentes expresaron una mejor percepción que los alumnos. Ambos coincidieron en señalar la necesidad de mejorar los sistemas de apoyo para alumnos estresados y no potenciar tanto la enseñanza basada en la memorización de conceptos teóricos

XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions

Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenariosS

Students’ Perceptions of Educational Climate in a Spanish School of Dentistry Using the Dundee Ready Education Environment Measure: A Longitudinal Study

Background: Educational Climate (EC) may determine teacher and student behaviour. Our aim was to evaluate EC longitudinally in a period of ‘curricular transition’ from traditional (teacher-centred learning) to Bologna curricula (interactive student-centred learning). Methods: The ‘Dundee Ready Education Environment Measure’ (DREEM) questionnaire was completed by 397 students from a Spanish School of Dentistry. Students’ perception was assessed in different courses and academic years. Results: The overall EC scale average was 115.70 ± 20.20 (57.85%) and all domain values showed a percentage >52%, which were interpreted as ‘positive and acceptable’. The EC mean was: 118.02 ± 17.37 (59.01%) for 2010–2011; 116.46 ± 19.79 (58.23%) for 2013–2014; 115.60 ± 21.93 (57.80%) for 2014–2015; 112.02 ± 22.28 (56.01%) for 2015–2016, interpreted as ‘more positive than negative EC’. The worst Learning domain scores corresponded to later academic years and may reflect the Bologna curriculum’s more intensive clinical training involving greater responsibility and self-learning. Conclusions: EC and its domains were perceived more positively than negatively. The Social domain was the most positively evaluated, while the Learning domain was the worstS

Automated description of the mandible shape by deep learning

Purpose: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs). Methods: We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model. Results: The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectivelyOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has received financial support from Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04 and Group with Growth Potential ED431B 2020-2022 GPC2020/27) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University SystemS