¿Por qué no evaluamos la evaluación? un esbozo para un sistema de evaluación entre iguales

Existen múltiples maneras de evaluar y distintos objetivos de la evaluación. Y el resultado de la evaluación es el que califica la calidad del trabajo realizado. Por tanto cualquier reflexión sobre la propia evaluación permitirá obtener mejores resultados, al juzgar los trabajos de la forma más justa posible. La evaluación entre iguales es una estrategia evaluativa cada vez más utilizada en el entorno universitario. Actualmente se está empleando en el ámbito de la docencia, pero cuenta con una amplia trayectoria en el campo de la investigación. Con este método, un autor revisa, generalmente de forma anónima, el trabajo de sus colegas que, a su vez, pueden conver-tirse en revisores de la obra de ese autor. Este sistema de evaluación entre iguales enriquece la propia evaluación, incluso puede llegar a ser una de las pocas maneras de evaluar al no existir una autoridad jerárquica en el tema. En esta investigación consideramos el proceso de evaluación entre iguales como un proceso de clasificación, en el que disponemos de varios clasificadores (los revisores) que, ante una entrada (el trabajo a revisar), deben asignar una determinada clase (calificación del trabajo). En este análisis se propone una métrica para valorar el grado de bondad de los revisores, con el objetivo de contribuir a mejorar la calidad de los procesos de evaluación entre iguales proporcionando una valoración más objetiva de la labor de los revisores

Early detection of learning difficulties. Tool for predicting student performance

Inspirados por las estrategias de detección precoz aplicadas en medicina, proponemos el diseño y construcción de un sistema de predicción que permita detectar los problemas de aprendizaje de los estudiantes de forma temprana. Partimos de un sistema gamificado para el aprendizaje de Lógica Computacional, del que se recolectan masivamente datos de uso y, sobre todo, resultados de aprendizaje de los estudiantes en la resolución de problemas. Todos estos datos se analizan utilizando técnicas de Machine Learning que ofrecen, como resultado, una predicción del rendimiento de cada alumno. La información se presenta semanalmente en forma de un gráfico de progresión, de fácil interpretación pero con información muy valiosa. El sistema resultante tiene un alto grado de automatización, es progresivo, ofrece resultados desde el principio del curso con predicciones cada vez más precisas, utiliza resultados de aprendizaje y no solo datos de uso, permite evaluar y hacer predicciones sobre las competencias y habilidades adquiridas y contribuye a una evaluación realmente formativa. En definitiva, permite a los profesores guiar a los estudiantes en una mejora de su rendimiento desde etapas muy tempranas, pudiendo reconducir a tiempo los posibles fracasos y motivando a los estudiantes.Inspired by the early detection strategies applied in medicine, we propose the design and construction of a prediction system to early detect learning problems of students. A gamified system to learn Computational Logic is the starting point from which a massive set of usage data and learning outcomes about problem solving is collected. All these data are analysed using Machine Learning techniques. As a result, a prediction of the performance of each student is obtained. The information is weekly presented as a progression chart, which is easily interpretable and contains valuable information. The resulting system has a high degree of automation, is progressive, provides results from the beginning of course with increasingly accurate predictions, uses learning outcomes as well as usage data, allows the evaluation and prediction of the acquired skills and abilities, and contributes to a truly formative assessment. In short, it allows teachers to guide students in their performance improvement from very early stages and can redirect possible failures in time and motivate students

Gamification of the learning process: lessons learned

Although several definitions of gamification can be found in the literature, they all have in common certain aspects: the application of strategies, models, dynamics, mechanics and elements of the games in other contexts than games, and the objective of producing a playful experience that fosters motivation, involvement and fun. In this paper, our approach gamifying the learning process of a subject is presented. Our experience throughout time in using games and gamification in learning have led us to propose, lately, a personalized, automated and gamified learning system. As a result of this experience and after several years of continuous feedback from our students, we have learned several lessons on how to approach the task of gamification. These lessons are summarized in the following concepts: fun, motivation, autonomy, progressiveness, feedback, error tolerance, experimentation, creativity and adaptation to the specific case. The final aim is sharing our experience and opening a debate about what key elements the gamification lies in

Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention

Lessons learned in gamification even when not called gamification

El término gamificación está de moda. Los gurús la sitúan como una tecnología emergente y disruptiva, que cambiará muchas de nuestras experiencias en campos tan alejados de los juegos como el empresarial, el marketing y la relación con los clientes. Y el entorno educativo no escapará a ello. En este artículo presentamos la experiencia de un grupo de profesores preocupados por la docencia, que llevamos años experimentando con los videojuegos y las experiencias lúdicas, y que de repente nos hemos encontrado con el término gamificación. Estas son las lecciones que hemos aprendido, que podemos enmarcar en el campo de la gamificación en educación, pero que derivan de una experiencia práctica, de un análisis desmenuzado y de una reflexión concienzuda. Pretendemos mostrar qué es lo realmente importante y qué puntos debemos tener en cuenta los profesores antes de lanzarnos al diseño gamificado de nuestra propuesta docente.The term gamification is fashionable. The gurus consider it as an emerging and disruptive technology that will change many of our experiences in fields as far from the games as business, marketing and customer relations. And the educational environment will not escape from it. This article presents the experience of a group of teachers concerned about teaching. We have experimented with video games and playful experiences for years and we have suddenly heard about the term gamification. These are the lessons we have learned, that can be placed in the field of gamification in education. They are derived from our practical experience, a thorough analysis and a deep reflection. We intend to show what is really important and what points should teachers consider before embarking on the gamified design of their teaching proposal

Improving the expressiveness of black-box models for predicting student performance

Early prediction systems of student performance can be very useful to guide student learning. For a prediction model to be really useful as an effective aid for learning, it must provide tools to adequately interpret progress, to detect trends and behaviour patterns and to identify the causes of learning problems. White-box and black-box techniques have been described in literature to implement prediction models. White-box techniques require a priori models to explore, which make them easy to interpret but difficult to be generalized and unable to detect unexpected relationships between data. Black-box techniques are easier to generalize and suitable to discover unsuspected relationships but they are cryptic and difficult to be interpreted for most teachers. In this paper a black-box technique is proposed to take advantage of the power and versatility of these methods, while making some decisions about the input data and design of the classifier that provide a rich output data set. A set of graphical tools is also proposed to exploit the output information and provide a meaningful guide to teachers and students. From our experience, a set of tips about how to design a prediction system and the representation of the output information is also provided

Panorámica: serious games, gamification y mucho más

La relación entre los videojuegos y el mundo de la educación es y ha sido tormentosa, con amores y odios, con sus altibajos. Pero lo que es indudable es que los videojuegos son una realidad en el mundo actual y una potente industria. Y además, los juegos siempre han jugado un papel fundamental en la educación. Aunque su incorporación a la actividad académica no ha sido todo lo ágil que hubiera sido conveniente, los videojuegos ya forman parte de la universidad. En este artículo vamos a presentar algunas de las iniciativas que hemos llevado a cabo desde que en el año 2002 incorporamos los videojuegos en nuestras actividades académicas, tanto docentes como investigadoras. MadUniversity es un videojuego que dio lugar a varios proyectos final de carrera de la Ingeniería en Informática. Screaming Racers es un videojuego diseñado y desarrollado para ser utilizado como plataforma de experimentación de técnicas en inteligencia artificial basadas en la neuroevolución. The Conference Interpreter (CoIn) es un videojuego para la práctica del inglés desarrollado para apoyar una tesis doctoral. GameLearning es una colección de minijuegos conceptuales para la adquisición de habilidades directivas. ABPgame es la aplicación de la metodología basada en proyectos a varias asignaturas de las titulaciones de Ingeniería en Informática y del Grado en Ingeniería Multimedia que realizan un proyecto común: un videojuego. PLMan es un sistema gamificado que ayuda a desarrollar habilidades de pensamiento lógico, a través del lenguaje Prolog. Nuestro objetivo es mostrar la utilidad de los videojuegos y sus múltiples aplicaciones en el entorno universitario: como objetos de aprendizaje por medio de videojuegos educativos (serious games); como proyectos informáticos complejos para ser desarrollados por nuestros estudiantes; como entorno de experimentación para comprobar la validez de las investigaciones en inteligencia artificial; y finalmente como filosofía a aplicar al campo de la educación, lo que se ha etiquetado como gamificación

Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention

Welfare implications on management strategies for rearing dairy calves: A systematic review. Part 1–feeding management

IntroductionCalves are very susceptible to stress in the early stages of life, and it is necessary to ensure maximum welfare. Feeding management has been identified as a major risk factor for calf health and welfare at this stage. However, the management protocol for calf rearing and its impact on animal welfare is unclear. A systematic review of different management strategies for rearing dairy calves according to the three spheres of animal welfare was conducted using an electronic search strategy. In this review, management strategies were studied to identify scientific gaps, to know the welfare problems of these animals in order to prioritize actions and future research and to study the interpretive approach of this management from the three welfare spheres.MethodsA protocol was used to analyze and extract information from the studies. Of the 1,783 publications screened, only 351 met the inclusion criteria for the management or welfare of calves' items.ResultsThe publications identified in the search can be divided into two main groups feeding and socialization, based on the main topic of the publication. The main topics that emerged from the search in the feeding management group were milk replacer, colostrum, and weaning, divided into the three main areas of biological functioning and health, natural life and affective states or cognitive judgement.DiscussionThe main issues to be addressed were the different types of feed consumed by animals from birth to weaning and the weaning management. It has been found that the most researched issues are colostrum and solid starter feed management. Unresolved issues were highlighted, such as the lack of a clear protocol for the administration of milk replacers to reduce hunger and the best management of weaning to reduce stress