Cómputo con palabras para la evaluación de pares estudiantiles en presentaciones orales

Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.La evaluación por pares en una presentación oral puede motivar y dar más sentido de responsabilidad a los estudiantes. En los últimos años, se han propuesto varios métodos para evaluar a los pares. En este artículo, se propone un método novedoso de evaluación en línea entre pares para la presentación oral utilizando la computación perceptiva. El resultado del sistema propuesto puede ser una puntuación numérica para la evaluación general de un estudiante en la presentación, que permite comparar y clasificar el desempeño del estudiante. además, del sistema se obtiene una evaluación lingüística que describe el desempeño del alumno. Se ha realizado un estudio de caso para mostrar la efectividad del método propuesto, luego se analizan y revisan los resultados

Cómputo con palabras para la evaluación de pares estudiantiles en presentaciones orales

La evaluación por pares en una presentación oral puede motivar y dar más sentido de responsabilidad a los estudiantes. En los últimos años, se han propuesto varios métodos para evaluar a los pares. En este artículo, se propone un método novedoso de evaluación en línea entre pares para la presentación oral utilizando la computación perceptiva. El resultado del sistema propuesto puede ser una puntuación numérica para la evaluación general de un estudiante en la presentación, que permite comparar y clasificar el desempeño del estudiante. además, del sistema se obtiene una evaluación lingüística que describe el desempeño del alumno. Se ha realizado un estudio de caso para mostrar la efectividad del método propuesto, luego se analizan y revisan los resultado

Practicality Issues in Using Fuzzy Approaches for Aggregating Students’ Academic Performance

AbstractEvaluation of student academic performance is one of the most important parts of the educational process. It has to be done for several important reasons. It also has to provide an evaluation in the form of score or grade that is interpretable by most people especially students, teachers, parents, employers and policy planners. The use of fuzzy approaches to perform student performance evaluation appears very appealing because of the use of natural language in representing the level of performance. However, in spite of such advantages, the existing proposed fuzzy approaches have not yet made any significant impact on the current evaluation systems. This paper presents a brief overview of evaluation of students’ performance using fuzzy approaches and discusses the main issues regarding the practicality of using such approaches for aggregating students’ academic performance

Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition

Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained by using a standard learning algorithm is varied on different data sets, which indicates that the same learning algorithm may train strong classifiers on some data sets but weak classifiers may be trained on other data sets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. In order to address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different data sets. In this paper, we propose a framework that involves CNN based feature extraction from the MINST data set and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of ≥ 98%

Εφαρμογή ασαφών συμπερασματικών μοντέλων στην διαγνωστική αξιολόγηση των μαθηματικών

Αξιολόγηση του μαθητή θεωρείται η συνεχής παιδαγωγική διαδικασία με την οποία παρακολουθείται η πορεία της μάθησης, προσδιορίζονται τα τελικά αποτελέσματά της και ο βαθμός επίτευξης των διδακτικών στόχων του μαθήματος και του προγράμματος σπουδών. Η διαγνωστική αξιολόγηση πραγματοποιείται στην αρχή του σχολικού έτους και βασικός σκοπός της είναι να προσδιοριστεί το γνωστικό επίπεδο των μαθητών, ώστε να προσαρμοστεί ανάλογα η διδασκαλία Στόχος της εργασίας αυτής η κατασκευή ενός ασαφούς (Fuzzy) «διαγνωστικού» μοντέλου της γνωστικής ικανότητας ενός μαθητή που ξεκινάει την Α’ τάξη του ενιαίου Λυκείου. . Η υπολογιστική εφαρμογή του συστήματος έγινε με την χρήση του Fuzzy Logic Toolbox το οποίο είναι μια συλλογή συναρτήσεων κατασκευασμένων στο αριθμητικό υπολογιστικό περιβάλλον του MatLab

Nature inspired framework of ensemble learning for collaborative classification in granular computing context

Due to the vast and rapid increase in the size of data, machine learning has become an increasingly popular approach of data classification, which can be done by training a single classifier or a group of classifiers. A single classifier is typically learned by using a standard algorithm, such as C4.5. Due to the fact that each of the standard learning algorithms has its own advantages and disadvantages, ensemble learning, such as Bagging, has been increasingly used to learn a group of classifiers for collaborative classification, thus compensating for the disadvantages of individual classifiers. In particular, a group of base classifiers need to be learned in the training stage, and then some or all of the base classifiers are employed for classifying unseen instances in the testing stage. In this paper, we address two critical points that can impact the classification accuracy, in order to overcome the limitations of the Bagging approach. Firstly, it is important to judge effectively which base classifiers qualify to get employed for classifying test instances. Secondly, the final classification needs to be done by combining the outputs of the base classifiers, i.e. voting, which indicates that the strategy of voting can impact greatly on whether a test instance is classified correctly. In order to address the above points, we propose a nature-inspired approach of ensemble learning to improve the overall accuracy in the setting of granular computing. The proposed approach is validated through experimental studies by using real-life data sets. The results show that the proposed approach overcomes effectively the limitations of the Bagging approach

Heuristic target class selection for advancing performance of coverage-based rule learning

Rule learning is a popular branch of machine learning, which can provide accurate and interpretable classification results. In general, two main strategies of rule learning are referred to as 'divide and conquer' and 'separate and con-quer'. Decision tree generation that follows the former strategy has a serious drawback, which is known as the replicated sub-tree problem, resulting from the constraint that all branches of a decision tree must have one or more common attributes. The above problem is likely to result in high computational complexity and the risk of overfitting, which leads to the necessity to develop rule learning algorithms (e.g., Prism) that follow the separate and conquer strategy. The replicated sub-tree problem can be effectively solved using the Prism algorithm , but the trained models are still complex due to the need of training an independent rule set for each selected target class. In order to reduce the risk of overfitting and the model complexity, we propose in this paper a variant of the Prism algorithm referred to as PrismCTC. The experimental results show that the PrismCTC algorithm leads to advances in classification performance and reduction of model complexity, in comparison with the C4.5 and Prism algorithms