Proposal for ontology based approach to fuzzy student model design

Intelligent tutoring system (ITS) is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide an adaptive and personalized tutoring suitable to each individual student based on his/her profile or characteristics. In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. The use of fuzzy logic in this context is to enable the computational analysis of the student's characteristics and learning behaviours in order to handle the uncertainty issues related to the student model design. Ontology is a vital tool for managing knowledge in a particular domain and is one of the recent techniques used to design the representation of student's cognitive state

Proposal for Ontology Based Approach to Fuzzy Student Model Design

Abstract -Intelligent tutoring system (ITS) is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian networks, Ontology, Genetic Algorithms and Software Agents) to provide an adaptive and personalized tutoring suitable to each individual student based on his/her profile or characteristics. In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. The use of fuzzy logic in this context is to enable the computational analysis of the student's characteristics and learning behaviours in order to handle the uncertainty issues related to the student model design. Ontology is a vital tool for managing knowledge in a particular domain and is one of the recent techniques used to design the representation of student's cognitive state

A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

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

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 model for providing emotion awareness and feedback using fuzzy logic in online learning

Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft

Expert System as Tools for Efficient Teaching and Learning Process in Educational System in Nigeria, First Step

In educational field, many of the expert systems application are embedded inside the Intelligent Tuttoring System (ITS) by using techniques from adaptive hypertext and hypermedia. Most of the systems usually will assist student in their learning by using adaptation techniques to personalize with the environment, prior of student and students ability to learn in terms of technology, expert system in education has expanded very consistently from micro computer to web based (Woodin, 2001) and agent based expert system, it can provide an excellent alternative to private tutoring at anytime from any place (Markham, 2001) where internet is provided. Also agent based expert system surely will help users by finding materials from the web based on users profile. Supposedly, agent expert system should have capability to diagnose the users and giving the results according to the problems. Besides the use of expert system in technology, it also had tremendous changes in the applying of methods and techniques. Starting from a simple rule based system, currently expert system techniques had adapted a fuzzy logic (Starek, Tomer, Bhaskar, and Garcia, 2001) and hybrid based technique (Pretzas, Hatzilygeroudis, and Koutsojannis, 2001)

Intelligent Tutoring System based on Subsumption Theory with Fuzzy accompaniment

Este artigo apresenta a proposta de um Sistema Tutor Inteligente (STI) para suporte a atividades de ensino, em ambiente virtual (web), cujo aporte teório-metodológico está embasado na Teoria da Aprendizagem Significativa de Ausubel. A arquitetura tradicional de STI foi estendida utilizando um componente baseado em Lógica Fuzzy, cuja função é monitorar o acompanhamento evolutivo do aprendiz acerca do conteúdo estudado. Este sistema ainda inclui o docente como seu usuário, seguindo as tendências da área de STI em incluir o professor como parceiro do módulo tutor na assistência personalizada aos alunos usuários do sistema.This paper describes a web Intelligent Tutoring System (ITS) based on Subsumtion Theory developed by Ausubel. The traditional ITS architecture was extended in order to support new component regarding student assistance and teacher interaction. These new modules were modeled using Fuzzy Logic