Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias

[EN] The peer-reviewing process fosters the participation of students in class by means of the evaluation of the activities carried out by their colleagues. In order for this procedure to be successful, it is necessary to introduce the activity and motivate it properly, as well as to define detailed and specific evaluation rubrics which gather all the learning goals. This study summarizes and analyses several peer-reviewing application cases performed during Sciences courses with the aim of detecting common patterns and differences between them. After comparing the grades obtained by following this process and reviewing several surveys about it, it can be concluded that, although some marginal discrepancies exist between the scores given by the professor and the students, their involvement in the evaluation process has a positive impact in their perception of the learning level and the adequacy of the evaluation system. In this way, the students are able to identify by themselves the strong and weak aspects of their work, which results also in an increase of their critical thinking. In addition, the final grade does not depend only on the criterion of the professor, but also on the interpretation of several previously established criteria done by the participants in the activity.[ES] El procedimiento de evaluación cruzada fomenta la participación en clase de los estudiantes mediante la valoración de las actividades llevadas a cabo por sus compañeros. Para que sea útil, es necesario introducir la actividad y motivarla adecuadamente, así como definir rúbricas detalladas y concretas que recojan todos los objetivos de aprendizaje. Este estudio recopila diferentes casos de aplicación de evaluación cruzada en asignaturas de ciencias, en donde se analizan las particularidades de cada caso con la finalidad de analizar patrones comunes y diferencias, así como plantear mejoras en su aplicación futura. A través de comparativas de notas y encuestas al alumnado se demuestra que, aun existiendo ligeras discrepancias entre las calificaciones otorgadas por los alumnos y el profesor, el nivel de implicación del alumno en el proceso evaluador redunda positivamente en su percepción del nivel aprendizaje y la adecuación del sistema de evaluación. Así, el alumno es capaz de identificar por sí mismo los puntos fuertes y débiles de su trabajo, redundando en un mayor espíritu crítico. Por otra parte, la calificación no depende solo del criterio de una persona, sino de la interpretación de varias personas sobre unos criterios comunes previamente establecidos.*Este trabajo ha sido realizado en el marco del proyecto docente UV-SFPIE PID-1640839: “Docencia y evaluación a distancia: uso de herramientas propias de la UV y externas para mejorar la metodología docente en línea e híbrida en el área de ciencias”.Ruescas, A.; Fernandez-Morán, R.; Moreno-Llácer, M.; Fernández-Torres, M.; Amorós-López, J.; Adsuara, J.; Esperante, D.... (2022). Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias. Editorial Universitat Politècnica de València. 314-326. https://doi.org/10.4995/INRED2022.2022.1587831432

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

An IP Core and GUI for Implementing Multilayer Perceptron with a Fuzzy Activation Function on Configurable Logic Devices

This paper describes the development of an Intellectual Property (IP) core in VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network (ANN) topology with up to 2 hidden layers, 128 neurons, and 31 inputs per neuron. Neural network models are usually developed by using programming languages, such as Matlab®. However, their implementation in configurable logic hardware requires the use of some other tools and hardware description languages, such as as VHDL. For easy migration, a Matlab Graphical User Interface (GUI) to automatically translate the ANN architecture to VHDL code has been developed. In addition, the use of an activation function based on fuzzy logic for the implementation of the MLP neural network simplifies the logic and improves the results. The environment was tested using a typical prediction problem, the Mackey-Glass series, where several ANN topologies were generated, tested and implemented in an FPGA. Results show the excellent agreement between the results provided by the software model and the hardware implementation

Graph Matching for Adaptation in Remote Sensing

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal very high resolution image classification and angular effects compensation, show the validity of the proposed method to match-related domains and enhance the application of cross-domains image processing techniques