A Perspective Projection Tutoring System With Motivation Diagnosis and Planning

2007 ◽  
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.

Recent Advances in Intelligent Tutoring Systems

2013 ◽  
pp. 631-647
Author(s):  
Joel J. P. C. Rodrigues ◽  
Pedro F. N. João ◽  
Isabel de la Torre Díez
Keyword(s):  
Learning Process ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Field Tests ◽  
Study Guide ◽  
Web Based ◽  
Tutoring Systems ◽  
Recent Advances ◽  
The Web

Intelligent Tutoring Systems (ITS) include interactive applications with some intelligence that supports the learning process. Some of ITS have had a very large impact on educational outcomes in field tests, and they have provided an important ground for artificial intelligence research. This chapter elaborates on recent advances in ITS and includes a case study presenting an ITS called EduTutor. This system was created for the Web-Based Aulanet Learning Management System (LMS). It focuses on subjects for the first cycle of studies of the Portuguese primary education system, between the first and the fourth year. It facilitates the perception of the learning process of each student, individually, in a virtual environment, as a study guide. Moreover, EduTutor has been designed and its architecture prepared for being easily integrated into higher levels of studies, different subjects, and several languages. Currently, it is used in the Aulanet LMS platform.

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.

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.

Developing Adaptive and Intelligent Tutoring Systems (AITS)

2014 ◽  
Vol 10 (4) ◽  
pp. 70-85
Author(s):  
Mohamed Hafidi ◽  
Tahar Bensebaa
Keyword(s):  
Multiple Intelligences ◽  
Learning Style ◽  
Learning Process ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Skill Level ◽  
Learning Performance ◽  
Difficulty Level ◽  
Tutoring Systems ◽  
The Individual

Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of learner's multiple intelligences, learner's skill level and learner's feedback when implementing personalized mechanisms. In this paper, the authors propose AITS based not only on the learner's multiple intelligences, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learner's multiple intelligences can promote personalized learning performance. Learner's skill level is obtained from pre-test result analysis, while learner's multiple intelligences are obtained from the analysis of questionnaire. After computing learning success rate of an activity, the system then modifies the difficulty level or the presentation of the corresponding activity to update courseware material sequencing. Learning process in this system is as follows. First, the system determines learning style and characteristics of the learner by an MI-Test and then makes the model. After that it plans a pre-evaluation and then calculates the score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post-evaluation. Finally the system offers guidance in learning other activities. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem- solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in “the best way.”

A Comparison of Three Learning Strategies in Intelligent Tutoring Systems

1996 ◽  
Vol 14 (4) ◽  
pp. 371-383 ◽  
Author(s):  
Claude Frasson ◽  
Esma Aimeur
Keyword(s):  
Knowledge Acquisition ◽  
Learning Strategies ◽  
Learning Process ◽  
Intelligent Tutoring Systems ◽  
Learning Strategy ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
New Approaches ◽  
Self Confidence ◽  
New Learning

New approaches in Intelligent Tutoring Systems imply a more active participation of the learner in the learning process. The motivation of the learner can be increased by interaction with a companion who strengthens the knowledge acquisition in a cooperation climate. In this article we introduce a new learning strategy called learning by disturbing intended to improve student self-confidence. We compare it to directive learning and peer learning, discussing the advantage and the inconvenience of each one. We present some experiments realized to show in which condition a strategy can be useful or not. We analyze and discuss results obtained.

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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