Tutoring Process in Emotionally Intelligent Tutoring Systems

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.

Download Full-text

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

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2014.18 ◽

2014 ◽

Vol 6 (2) ◽

pp. 138-146 ◽

Cited By ~ 3

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.

Download Full-text

Intelligent Tutoring Systems

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0144 ◽

2017 ◽

Vol 26 (4) ◽

pp. 717-727 ◽

Cited By ~ 3

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.

Download Full-text

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

International Journal of Intelligent Information Technologies ◽

10.4018/jiit.2010100904 ◽

2010 ◽

Vol 6 (1) ◽

pp. 46-70 ◽

Cited By ~ 8

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.

Download Full-text

A Perspective Projection Tutoring System With Motivation Diagnosis and Planning

Volume 2: 27th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2007-35432 ◽

2007 ◽

Cited By ~ 1

Author(s):

Yong Se Kim ◽

Hyun Jin Cha ◽

Tae Bok Yoon ◽

Jee-Hyoung Lee

Keyword(s):

Fuzzy Logic ◽

Student Success ◽

Learning Process ◽

Intelligent Tutoring Systems ◽

Multiagent System ◽

Emotional State ◽

Intelligent Tutoring ◽

Perspective Projection ◽

Tutoring System ◽

Tutoring Systems

Motivation is a paramount factor to student success. Although it is well known that the learner’s motivation and emotional state in educational contexts are very important, they have not been fully addressed in intelligent tutoring systems (ITS). In this paper, a method for integrated motivation diagnosis and motivational planning is described in a manner applied to an operable system. For the motivational diagnosis rules, three different channels of data (performance from interaction with the system, verbal communication, and feedbacks) are combined. For the motivational planning rules, four different strategies (different learning process, helps, different teaching strategies, and arousal questions or feedbacks) are combined. By applying the mechanisms, a tutoring system for the topic of perspective projection with motivation diagnosis and motivational planning on a multiagent system with fuzzy logic has been implemented.

Download Full-text

Student Modeling in an Intelligent Tutoring System

Intelligent Tutoring Systems in E-Learning Environments ◽

10.4018/978-1-61692-008-1.ch011 ◽

2011 ◽

pp. 208-236 ◽

Cited By ~ 1

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.

Download Full-text