Fuzzy multi criteria evaluation for performance of bus companies

A multi criteria decision making in ranking the bus companies using fuzzy rule is proposed. The proposed method uses the application of fuzzy sets and approximate reasoning in deciding the ranking of the performance of several bus companies. The proposed method introduces data normalization using similarity function which dampens extreme values that exist in the data. The use of the model is suitable in evaluating situation that involves subjectivity, vagueness and imprecise information. Experimental results are comparable to several previous methods

A fuzzy-based approach for classifying students' emotional states in online collaborative work

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft

A contribution to students’ assessment adjusts of multiple choice questionnaires with fuzzy logic

In University environment, it is common to use multiple-choice objective tests with three or four possible answers, of which only one is correct and the rest are erroneous. In this type of tests, usually the wrong answers are penalized in order to avoid the effect of the random answers. However, there are questions that hardly students answer since their difficulty is high. On the other hand, there are also questions that answer virtually all students since their difficulty is simple. While sometimes the course professor chooses to suppress these questions, it is also common to leave them as part of the calculation of the overall score. This communication proposes a way of, without suppressing any question, making a readjustment of the grades based on fuzzy logic techniques. To do this, it is considered, on the one hand, the initial grade obtained by each student and, on the other, the total difficulty index of the test. With these two variables, an approximation can be made to a system of linguistic variables that allows correcting the final grades of each student based on the objective difficulty of the test and a set of rules established by the professor. This will revert to greater “justice” in students’ mark system, since it will be a function of the difficulty of the test

Fuzzy logic-based assessment adjust of multiple choice questionnaires

In University environment, it is common to use multiple-choice objective tests with three or four possible answers, of which only one is correct and the rest are erroneous. In this type of tests, usually the wrong answers are penalized in order to avoid the effect of the random answers. However, there are questions that hardly students answer since their difficulty is high. On the other hand, there are also questions that answer virtually all students since their difficulty is simple. While sometimes the course professor chooses to suppress these questions, it is also common to leave them as part of the calculation of the overall score. This communication proposes a way of, without suppressing any question, making a readjustment of the grades based on fuzzy logic techniques. To do this, it is considered, on the one hand, the initial grade obtained by each student and, on the other, the total difficulty index of the test. With these two variables, an approximation can be made to a system of linguistic variables that allows correcting the final grades of each student based on the objective difficulty of the test and a set of rules established by the professor. This will revert to greater “justice” in students’ mark system, since it will be a function of the difficulty of the test.Postprint (published version

EVALUATION OF STUDENT ACADEMIC PERFORMANCE USING ADAPTIVE NEURO-FUZZY APPROACH

The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas. Due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory and fuzzy if-then rules. The advantages of the combination of ANN and FIS are apparent. The developed method uses a fuzzy system to support neural networks to enhance some of its characteristics like flexibility, speed and adaptability which is called the Adaptive Neuro-fuzzy inference system (ANFIS). Evaluating and assessing the student academic performance is not an easy task, especially when it involves many attributes or factors. Moreover, the knowledge of the human experts is acquired to determine the criteria of students’ academic performance and the decisions about their level of assimilation but most of the information is incomplete and vague. To overcome the problem, this work evaluates the student’s academic performance based on ANFIS tools which was implemented on MATLAB 7.6.0 (R2008a). The method produces crisp numerical outcomes that evaluate the student’s academic performance. The student performance after the training of the two inputs was at the average for semester1 and semester 2.Â

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