Comparison of AESA and LAESA search algorithms using string and tree-edit-distances

Although the success rate of handwritten character recognition using a nearest neighbour technique together with edit distance is satisfactory, the exhaustive search is expensive. Some fast methods as AESA and LAESA have been proposed to find nearest neighbours in metric spaces. The average number of distances computed by these algorithms is very low and does not depend on the number of prototypes in the training set. In this paper, we compare the behaviour of these algorithms when string and tree-edit-distances are used.Work partially supported by the spanish CICYT TIC2000-1599-C02 and TIC2000-1703-CO3-02

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.This work was partially supported by the Spanish CICyT through the project TIN2013-48152-C2-1-R, the Consejería de Educación de la Comunidad Valenciana through Project PROMETEO/2012/017 and a FPU fellowship (AP2012-0939) from the Spanish Ministerio de Educación Cultura y Deporte

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

Cross-species tests of 45 microsatellite loci isolated from different species of ungulates in the Iberian red deer (Cervus elaphus hispanicus) to generate a multiplex panel

The Iberian red deer (Cervus elaphus hispanicus) is an emblematic game species in Spain. To generate a battery of polymorphic markers for multiplex polymerase chain reactions for the Spanish red deer, 45 loci isolated in different species of ungulates were tested. Of the primers tested, 27 amplified but only 21 were polymorphic. Eleven of these markers were subsequently optimized for multiplex in four polymerase chain reactions. This allows analysing several molecular markers jointly to substantially reduce costs. Finally, we report descriptive summary statistics such as number of alleles for the former and also test of disequilibria and heterozygosity for the latter. © 2008 The Authors.Peer Reviewe

A new iterative algorithm for computing a quality approximate median of strings based on edit operations

This paper presents a new algorithm that can be used to compute an approximation to the median of a set of strings. The approximate median is obtained through the successive improvements of a partial solution. The edit distance from the partial solution to all the strings in the set is computed in each iteration, thus accounting for the frequency of each of the edit operations in all the positions of the approximate median. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximate median. The algorithm finishes when all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes and the Copenhagen chromosome database show that the quality of the approximate median string is similar to benchmark approaches but achieves a much faster convergence.This work is partially supported by the Spanish CICYT under project DPI2006-15542-C04-01, the Spanish MICINN through project TIN2009-14205-CO4-01 and by the Spanish research program Consolider Ingenio 2010: MIPRCV (CSD2007-00018)

Influence of personality and modality on peer assessment evaluation perceptions using Machine Learning techniques

The successful instructional design of self and peer assessment in higher education poses several challenges that instructors need to be aware of. One of these is the influence of students’ personalities on their intention to adopt peer assessment. This paper presents a quasi-experiment in which 85 participants, enrolled in the first-year of a Computer Engineering programme, were assessed regarding their personality and their acceptance of three modalities of peer assessment (individual, pairs, in threes). Following a within-subjects design, the students applied the three modalities, in a different order, with three different activities. An analysis of the resulting 1195 observations using ML techniques shows how the Random Forest algorithm yields significantly better predictions for three out of the four adoption variables included in the study. Additionally, the application of a set of eXplainable Artificial Intelligence (XAI) techniques shows that Agreeableness is the best predictor of Usefulness and Ease of Use, while Extraversion is the best predictor of Compatibility, and Neuroticism has the greatest impact on global Intention to Use. The discussion highlights how, as it happens with other innovations in educational processes, low levels of Consciousness is the most consistent predictor of resistance to the introduction of peer assessment processes in the classroom. Also, it stresses the value of peer assessment to augment the positive feelings of students scoring high on Neuroticism, which could lead to better performance. Finally, the low impact of the peer assessment modality on student perceptions compared to personality variables is debated.This work has been partially funded by the University of Alicante’s Redes-I3CE de investigación en docencia universitaria del Instituto de Ciencias de la Educación (REDES-I3CE-2020-5069), by the EU Erasmus+ Programme (EduTech (609785-EPP-1-2019-1-ES-EPPKA2-CBHE-JP) and SkoPS (2020-1-DE01-KA226HE-005772) projects), by the Spanish Ministry of Science and Innovation (Access@IoT (PID2019-111196RB-I00) project), by the GVA (AICO/2020/143) project, and by the UCLM group cofinanced with ERDF funds (research grant 2021-GRIN-30993)

An improved fast edit approach for two-string approximated mean computation applied to OCR

This paper presents a new fast algorithm for computing an approximation to the mean of two strings of characters representing a 2D shape and its application to a new Wilson-based editing procedure. The approximate mean is built up by including some symbols from the two original strings. In addition, a Greedy approach to this algorithm is studied, which allows us to reduce the time required to compute an approximate mean. The new dataset editing scheme relaxes the criterion for deleting instances proposed by the Wilson editing procedure. In practice, not all instances misclassified by their near neighbors are pruned. Instead, an artificial instance is added to the dataset in the hope of successfully classifying the instance in the future. The new artificial instance is the approximated mean of the misclassified sample and its same-class nearest neighbor. Experiments carried out over three widely known databases of contours show that the proposed algorithm performs very well when computing the mean of two strings, and outperforms methods proposed by other authors. In particular, the low computational time required by the heuristic approach makes it very suitable when dealing with long length strings. Results also show that the proposed preprocessing scheme can reduce the classification error in about 83% of trials. There is empirical evidence that using the Greedy approximation to compute the approximated mean does not affect the performance of the editing procedure.This work is partially supported by the Spanish CICYT under project DPI2006-15542-C04-01, the Spanish MICINN through project TIN2009-14205-CO4-01 and by the Spanish research program Consolider Ingenio 2010: MIPRCV (CSD2007-00018)

Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques

Academic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been partially funded by the Instituto de Ciencias de la Educación (ICE) of the University of Alicante through their ‘Programa de redes de investigación en docencia universitaria’ in the 2023/2024 edition (Red 5942), and the UCLM group, cofinanced with ERDF funds (research grant 2022-GRIN-34113)

La enseñanza de las Matemáticas en Secundaria en otros paises en la actualidad

Cada cierto tiempo nuestro país se enfrenta a nuevos planes de estudio que tratan de mejorar aquellas faltas o lagunas que se detectan en el/los modelos previos. Además encuestas e informes internacionales aportan datos acerca del nivel académico de los estudiantes de diferentes países. En este trabajo, se pretende recabar información sobre los modelos que utilizan otros países de nuestro entorno respecto a las matemáticas en Secundaria (temario, metodología y evaluación), y obtener algunas conclusiones sobre los mismos.Departamento de Matemática AplicadaMáster en Profesor de Educación Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanzas de Idioma