Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Neural network-based fuzzy modeling of the student in intelligent tutoring systems

An empirical approach that makes use of neuro-fuzzy synergism to evaluate the students in the context of an intelligent tutoring system is presented. In this way, a qualitative model of the student is generated, which is able to evaluate information regarding student's knowledge and cognitive abilities in a domain area. The neuro-fuzzy model has been tested on a prototype tutoring system in the physics domain of the vertical projectory motions and the results have been very satisfactory

A neuro-fuzzy approach to detect student's motivation

In this paper the fuzzy knowledge representation of a neural network-based fuzzy model is presented. The model is used to assess student's motivational state in a discovery learning environment. Student's observable behavior and motivational factors are described with linguistic variables. The inputs of the model are tailored from real students' data, with the assistance of a group of expert teachers. Results of our preliminary study were encouraging, since data obtained from real students' log files, have been successfully used to form the membership functions that assign membership degrees to the linguistic values of the linguistic variables

Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model

In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students' learning style in the discovery learning environment "Vectors in Physics and Mathematics" is presented. Fuzzy logic is used to provide a linguistic description of students' behavior and learning characteristics, as they have been elicited from teachers, and to handle the inherent uncertainty associated with teachers' subjective assessments. Neural networks are used to add learning and generalization abilities to the fuzzy model by encoding teachers' experience through supervised neural-network learning. The neural network-based fuzzy diagnostic model is a general diagnostic model which is implemented in an Intelligent Learning Environment by eliciting teachers' expertise regarding students' characteristics based on real students' observation and on data being collected from students' interaction. The model has been successfully implemented, trained and tested in the learning environment "Vectors in Physics and Mathematics" by using the recommendations of a group of five experienced teachers. The performance of our model in real classroom conditions has been evaluated during an experiment with an experienced Physics teacher and 49 students of secondary school attending Physics lessons. © 2006 Elsevier Ltd. All rights reserved

Using simulated students for machine learning

In this paper we present how simulated students have been generated in order to obtain a large amount of labeled data for training and testing a neural network-based fuzzy model of the student in an Intelligent Learning Environment (ILE). The simulated students have been generated by modifying real students' records and classified by a group of expert teachers regarding their learning style category. Experimental results were encouraging, similar to experts' classifications. © Springer-Verlag 2004

Intelligent and interactive web-based tutoring system in engineering education : reviews, perspectives and development

Due to the rapid growth of the use of computers and increasing use of the Internet in education a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The Web-based intelligent learning has become more effective in the past decade due to increasing use of the Internet ineducation. A literature search indicates that there is a lack of relevant comprehensive research concerning the efficiency of computer-assisted instructions used in engineering education. The main focus of the research described in this chapter is on the comprehensive review of design and development of the Web-based authoring tool for an Intelligent Tutoring System in engineering education. The chapter outlines and discusses important issues of the development of intelligent Tutoring System (ITS) in engineering education with an example of the development of a Web-Based Computer-Assisted Tutorials and Laboratory Procedures (WCALP)

Automatic detection of learning styles: state of the art

A learning style describes the attitudes and behaviors, which determine an individual´s preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students´ lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.Fil: Feldman, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin