El Arte de coordinar actividades colaborativas con un solo clic

El rol del profesor cambia cuando hace uso de las TIC, su figura tiende a planificar y guiar situaciones de aprendizaje más que a ser un mero transmisor de información como en el pasado. El disponer del conocimiento necesario sobre las herramientas adecuadas para realizar la labor de seguimiento y control es fundamental para descongestionar al docente en estas labores. Una vez se planifica una asignatura el seguimiento de la misma es muy importante por lo que este artículo presenta una forma innovadora de gestionar la distribución, control y evaluación de actividades para un gran número de alumnos en clases presenciales masificadas. Pretende ser una guía para adquirir unas nociones básicas hacia la automatización de las tareas de coordinación basadas en servicios gratuitosWeb 2.0 de Google y una orientación para saber qué servicios usar cuando queremos automatizar procesos repetitivos.SUMMARY -- The teacher’s role has changed with the introduction of ICT, it tends to plan and guide learning situations rather than being a mere transmitter of information as in the past. Nowadays, teachers must also know how to use the latest management and monitoring tools in order to relieve their daily work. Once a course is planned, track the same is very important, so this paper presents an innovative way to manage the distribution, monitoring and evaluation of activities for large numbers of students in overcrowded classes. This text also intends to be a guide on how to automate coordination tasks and repetitive processes using Web 2.0 and the free services of Google

On the Performance Of the Thread-Multiple Support Level In Thread-Based MPI

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.Exascale systems are likely to have orders of magnitude less memory per core than current systems (though still large amounts of memory). As the amount of memory per core is dropping, going to thread-based models might be an unavoidable step towards the exascale milestone. AzequiaMPI is a thread-based open source full conformant implementation of MPI-1.3 for shared memory. We expose the techniques introduced in AzequiaMPI that, first, simplify the implementation and second, make the thread-based model to significantly improve the bandwidth of process-based implementations. Current version is also compliant with the MPI_THREAD_MULTIPLE thread-safety level, a feature of MPI-2.0 standard. The well known Thakur and Gropp MPI_THREAD_MULTIPLE tests show that both latency and bandwidth figures of AzequiaMPI significantly improve that of MPC-MPI, MPICH and Open MPI in an eight cores Intel Xeon E5620 Nehalem machine.The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, ’Network for Sustainable Ultrascale Computing (NESUS)’

Improving the Performance of the MPI_Allreduce Collective Operation through Rank Renaming

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.Collective operations, a key issue in the global efficiency of HPC applications, are optimized in current MPI libraries by choosing at runtime between a set of algorithms, based on platform-dependent beforehand established parameters, as the message size or the number of processes. However, with progressively more cores per node, the cost of a collective algorithm must be mainly imputed to process-to-processor mapping, because its decisive influence over the network traffic. Hierarchical design of collective algorithms pursuits to minimize the data movement through the slowest communication channels of the multi-core cluster. Nevertheless, the hierarchical implementation of some collectives becomes inefficient, and even impracticable, due to the operation definition itself. This paper proposes a new approach that departs from a frequently found regular mapping, either sequential or round-robin. While keeping the mapping, the rank assignation to the processes is temporarily changed prior to the execution of the collective algorithm. The new assignation makes the communication pattern to adapt to the communication channels hierarchy. We explore this technique for the Ring algorithm when used in the well-known MPI_Allreduce collective, and discuss the obtained performance results. Extensions to other algorithms and collective operations are proposed.The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, ’Network for Sustainable Ultrascale Computing (NESUS)’, and by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETACIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain

Automatic detection of inconsistencies between numerical scores and textual feedback in peer-assessment processes with machine learning

The use of peer assessment for open-ended activities has advantages for both teachers and students. Teachers might reduce the workload of the correction process and students achieve a better understanding of the subject by evaluating the activities of their peers. In order to ease the process, it is advisable to provide the students with a rubric over which performing the assessment of their peers; however, restricting themselves to provide only numerical scores is detrimental, as it prevents providing valuable feedback to others peers. Since this assessment produces two modalities of the same evaluation, namely numerical score and textual feedback, it is possible to apply automatic techniques to detect inconsistencies in the evaluation, thus minimizing the teachers’ workload for supervising the whole process. This paper proposes a machine learning approach for the detection of such inconsistencies. To this end, we consider two different approaches, each of which is tested with different algorithms, in order to both evaluate the approach itself and find appropriate models to make it successful. The experiments carried out with 4 groups of students and 2 types of activities show that the proposed approach is able to yield reliable results, thus representing a valuable approach for ensuring a fair operation of the peer assessment process

Estrategias para programar la detección de plagios en actividades basadas en texto

La detección de plagios en los trabajos entregados por los alumnos es un problema que ha existido tradicionalmente cuando se entregaban en formato papel pero que en los últimos años se ha incrementado debido a la gran cantidad de información que existe en Internet, a la facilidad para encontrarla usando buscadores y a la entrega electrónica de los trabajos o actividades (ciberplagio). Incluso existen plataformas en Internet que estructuran y ofrecen gratuitamente los trabajos para que se puedan descargar. En este artículo se proponen varias estrategias orientadas a implementar un programa para uso personal que detecte uno de los tipos de plagio más extendidos actualmente como es copiar y pegar fragmentos de textos de Internet. Estas propuestas se estudian para la detección de plagio en trabajos de diferente índole, incluyendo memorias, diapositivas y páginas web. El sistema devuelve un índice de coincidencia por entrega, de esta forma el profesor puede identificar claramente las copias y centrar su esfuerzos en revisar solamente el contenido de las tareas originales.Detection of plagiarism in students’ homework is a problem that already existed when they were submited in paper form. In recent years, it has increased due to the large amount of information available on the In-ternet, to the ease of use of search engines to find infor-mation, and to the electronic submission (cyberplagiarism). There are even Internet platforms that prepare and offer free homework to be downloaded. This article proposes several strategies to implement a program for personal use to detect one of the currently most widespread types of plagiarism as is copying and pasting fragments of texts from the Internet. These proposals are targeted to detect plagiarism in papers of different type, including reports, slides and web pages. This system returns an matching index for each delivery, thus teachers can clearly identify copies and focus efforts on reviewing only original works

An overview of ensemble and feature learning in few-shot image classification using siamese networks

Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69% to 78% with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4% to 9%.First author is supported by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256. This research work was partially funded by the Spanish “Ministerio de Ciencia e Innovación” and the European Union “NextGenerationEU/PRTR” programmes through project DOREMI (TED2021-132103A-I00). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

El arte de coordinar actividades colaborativas con un solo clic

El rol del profesor cambia cuando hace uso de las TIC, su figura tiende a planificar y guiar situaciones de aprendizaje más que a ser un mero transmisor de información como en el pasado. El disponer del conocimiento necesario sobre las herramientas adecuadas para realizar la labor de seguimiento y control es fundamental para descongestionar al docente en estas labores. Una vez se planifica una asignatura el seguimiento de la misma es muy importante por lo que este artículo presenta una forma innovadora de gestionar la distribución, control y evaluación de actividades para un gran número de alumnos en clases presenciales masificadas. Pretende ser una guía para adquirir unas nociones básicas hacia la automatización de las tareas de coordinación basadas en servicios gratuitos Web 2.0 de Google y una orientación para saber qué servicios usar cuando queremos automatizar procesos repetitivos.The teacher’s role has changed with the introduction of ICT, it tends to plan and guide learning situations rather than being a mere transmitter of information as in the past. Nowadays, teachers must also know how to use the latest management and monitoring tools in order to relieve their daily work. Once a course is planned, track the same is very important, so this paper presents an innovative way to manage the distribution, monitoring and evaluation of activities for large numbers of students in overcrowded classes. This text also intends to be a guide on how to automate coordination tasks and repetitive processes using Web 2.0 and the free services of Google

Statistical semi-supervised system for grading multiple peer-reviewed open-ended works

In the education context, open-ended works generally entail a series of benefits as the possibility of develop original ideas and a more productive learning process to the student rather than closed-answer activities. Nevertheless, such works suppose a significant correction workload to the teacher in contrast to the latter ones that can be self-corrected. Furthermore, such workload turns to be intractable with large groups of students. In order to maintain the advantages of open-ended works with a reasonable amount of correction effort, this article proposes a novel methodology: students perform the corrections using a rubric (closed Likert scale) as a guideline in a peer-review fashion; then, their markings are automatically analyzed with statistical tools to detect possible biased scorings; finally, in the event the statistical analysis detects a biased case, the teacher is required to intervene to manually correct the assignment. This methodology has been tested on two different assignments with two heterogeneous groups of people to assess the robustness and reliability of the proposal. As a result, we obtain values over 95% in the confidence of the intra-class correlation test (ICC) between the grades computed by our proposal and those directly resulting from the manual correction of the teacher. These figures confirm that the evaluation obtained with the proposed methodology is statistically similar to that of the manual correction of the teacher with a remarkable decrease in terms of effort.This work has been supported by the Vicerrectorado de Calidad e Innovación Educativa-Instituto de Ciencias de la Educación of the Universidad de Alicante (2016-17 edition) through the Programa de Redes-I3CE de investigación en docencia universitaria (ref. 3690)

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”

Sistema para la detección temprana de anomalías en la evaluación usando técnicas de aprendizaje automático

Uno de procesos más importantes en casi todos los modelos de enseñanza universitaria es la evaluación. Los criterios que se establecen en una asignatura orientan la forma en la que se obtiene la calificación final del alumno. Por este motivo es importante realizar un seguimiento continuado del aprendizaje del estudiante y de sus calificaciones, permitiendo de este modo la detección de anomalías para proceder con una intervención inmediata que permita corregir la situación. Normalmente, en los primeros cursos universitarios el número de alumnos es elevado, lo que redunda en el detrimento del seguimiento que se le puede realizar a los estudiantes por parte del profesor. En este trabajo se propone un sistema para predecir la calificación de un estudiante en una determinada actividad, de forma que se notifique al profesor cuando la calificación real se aleje suficientemente del valor predicho. Para esto se ha realizado un estudio de 24 algoritmos de inteligencia artificial, seleccionando finalmente los más adecuados para el caso de estudio realizado. Los resultados experimentales muestran la utilidad del método propuesto y cómo los algoritmos basados en máquinas de vectores soporte o los de aumentado de gradiente extremo son los que mejores resultados obtienen.One of the most important processes in almost all university education models is evaluation. The criteria established in a subject guide how the student’s final grade is obtained. Therefore, it is important to continuously monitor the student’s learning process and grades, thus allowing the detection of anomalies to proceed with an immediate intervention to correct the situation. Typically, the first university courses have a high number of students, which is detrimental to the tracking that can be done by the teacher. In this paper, we propose an approach to predict the next grade of a student in a certain activity, so that the teacher is notified in case the actual grade is different enough from the predicted one. To this end, an experimental study of 24 artificial intelligence algorithms, selecting the most suitable ones for our case of study. The experimental results show the goodness of the proposed approach, and that the algorithms based on support vector machines or those of extreme gradient augmentation are the ones that best fit the considered data.El presente trabajo contó con una ayuda del Programa de Redes-I3CE de investigación en docencia universitaria del Instituto de Ciencias de la Educación de la Universidad de Alicante (convocatoria 2018-19). Ref.: REDES-I3CE-2018-436