Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

2010 ◽  
Vol 6 (1) ◽  
pp. 46-70 ◽  
Author(s):  
Kiran Mishra ◽  
R.B. Mishra
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Teaching Strategy ◽  
Intelligent Tutoring System ◽  
Selection Index ◽  
Tutoring Systems ◽  
Analytical Reasoning ◽  
The Subject ◽  
Effective Models ◽  
Selection Of

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

2012 ◽  
pp. 47-71
Author(s):  
Kiran Mishra ◽  
R. B. Mishra
Keyword(s):  
Intelligent Tutoring ◽  
Teaching Strategy ◽  
Intelligent Tutoring System ◽  
Selection Index ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Analytical Reasoning ◽  
The Subject ◽  
Effective Models ◽  
Selection Of

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

scholarly journals Intelligent Tutoring Systems

2017 ◽  
Vol 26 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Vladimír Bradáč ◽  
Kateřina Kostolányová
Keyword(s):  
Intelligent Tutoring Systems ◽  
Technological Development ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tertiary Sector ◽  
Tutoring Systems ◽  
Number Of Ends ◽  
E Learning ◽  
Education Of Students

AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.

Tutoring Process in Emotionally Intelligent Tutoring Systems

2016 ◽  
pp. 1094-1110 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Emotional State ◽  
Intelligent Tutoring ◽  
Point Of View ◽  
Research Groups ◽  
Tutoring Systems ◽  
Knowledge States ◽  
Close Relationship ◽  
Emotionally Intelligent ◽  
Selection Of

Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

scholarly journals Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

Tutoring Process in Emotionally Intelligent Tutoring Systems

2014 ◽  
Vol 4 (1) ◽  
pp. 72-85 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Emotional State ◽  
Intelligent Tutoring ◽  
Point Of View ◽  
Research Groups ◽  
Tutoring Systems ◽  
Knowledge States ◽  
Close Relationship ◽  
Emotionally Intelligent ◽  
Selection Of

Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning

1995 ◽  
Vol 10 (1) ◽  
pp. 52-62
Author(s):  
Marios C. Angelides ◽  
Amelia K.Y. Tong
Keyword(s):  
Music Theory ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Individual Student ◽  
Tutoring System ◽  
Music Learning ◽  
Tutoring Systems

Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.

Student Modeling in an Intelligent Tutoring System

2011 ◽  
pp. 208-236 ◽  
Author(s):  
Mingyu Feng ◽  
Neil Heffernan ◽  
Kenneth Koedinger
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Student Modeling ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Fine Grained ◽  
Knowledge States ◽  
Complex Models ◽  
Cognitively Diagnostic

Student modeling and cognitively diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (its). Its needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. This chapter reviews our effort on modeling student’s knowledge in the ASSISTment project. Intelligent tutors have been mainly used to teach students. In the ASSISTment project, we have emphasized using the intelligent tutoring system as an assessment system that provides instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as its then like try to hit a moving target. So our results are surprising that by providing tutoring to students while they are assessed we actually prove the assessment of students’ knowledge. Additionally, in this article, we present encouraging results about a fine-grained skill model with that system that is able to predict state test scores. We conclude that using intelligent tutoring systems to do assessment seems like a reasonable way of dealing with the dilemma that every minute spent testing students takes time away from instruction.

Designing an Intelligent Tutoring System Interface to Account for Personality Differences

1994 ◽  
Vol 38 (13) ◽  
pp. 789-792
Author(s):  
Pauline K. Cushman
Keyword(s):  
Subject Matter ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Personality Differences ◽  
The Way

Intelligent Tutoring Systems have been designed for a variety of purposes. Much of the design effort has been aimed at the actual subject matter. Often ignored has been the critical nature of the interface. If the way people interact with computers is directly related to their personality, then systems should respond differently to different people. This paper describes the design of an interface for an Intelligent Tutoring System that, given the student's personality, will make adjustments in the style of interaction.

scholarly journals DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS

2014 ◽  
Vol 6 (2) ◽  
pp. 138-146 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Emotional Component ◽  
Human Teachers ◽  
New Generation ◽  
Emotionally Intelligent

Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.

Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording

2011 ◽  
Vol 26 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Fred Phillips ◽  
Benny G. Johnson
Keyword(s):  
Education Research ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Accounting Education ◽  
Online Homework ◽  
Learning Effects ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Transaction Analysis

ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Implications for accounting instructors and researchers are discussed.

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