Cognitive Tutor

ISBN-13: 9789811951992 / Angielski / Miękka / 2023

Ninni Singh;Vinit Kumar Gunjan;Jacek M. Zurada

Opis

This book illustrates the design, development, and evaluation of personalized intelligent tutoring systems that emulate human cognitive intelligence by incorporating artificial intelligence. Artificial intelligence is an advanced field of research. It is particularly used in the field of education to increase the effectiveness of teaching and learning techniques. With the advancement of internet technology, there is a rapid growth in web based distance learning modality. This mode of learning is better known as the e-learning system. These systems present low intelligence because they offer a pre-identified learning frame to their learners. The advantage of these systems is to offer to learn anytime and anyplace without putting emphasis on a learner's needs, competency level, and previous knowledge. Every learner has different grasping levels, previous knowledge, and preferred mode of learning, and hence, the learning process of one individual may significantly vary from other individuals. This book provides a complete reference for students, researchers, and industry practitioners interested in keeping abreast of recent advancements in this field. It encompasses cognitive intelligence and artificial intelligence which are very important for deriving a roadmap for future research on intelligent systems.  

Spis treści

Part 1:Introduction

1.      Introduction         

1.1 Need of an Intelligent Tutoring System.

1.2 A Growing Field: Intelligent Tutoring System

1.3 Intelligent Tutoring System Architecture

2. Organization of Content of this Book

 

Part 2: Domain Modeling

2.1  Introduction to Domain Model

2.2  The Epistemological Perspective of Domain Knowledge

2.3  Preliminary Research on Domain Model in ITS

2.4  Experiential (Tacit Domain Knowledge)

2.5  Experiential Knowledge Acquisition Approaches

2.5.1        Cognitive Map

2.5.2        Causal Map

2.5.3        Self Q

2.5.4        Semi-Structured

2.6  Summary

 

Part 3: Pedagogy Modeling

3.1  Introduction to Pedagogy Model

3.2  Preliminary Research on Pedagogy Model in ITS

3.3  Path Sequencing of Learning Material in Learning Systems

3.4  Impact of Emotion Capturing in Learning System

3.4.1        Emotion Recognition in Learning System

3.5  Summary

 

Part 4: Building SeisTutor Intelligent Tutoring System for Experiential Learning Domain

4.1.            Introduction

4.2.            Seismic Data Interpretation: as a Experiential Learning Domain

4.3.            Development of Adaptive Domain Model

4.3.1.      Phase 1: Tacit Knowledge Acquisition and Characterization

4.3.2.      Phase 2: Knowledge Representation: Multilevel hierarchical model

4.4.            Summary

 

Part5: Pedagogy model for Building SeisTutor Intelligent Tutoring System.

5.1.      Introduction

5.2.      Workflow of SeisTutor

            5.2.1.   Development of Custom-Tailored Curriculum

            5.2.2.   Development of Tutoring Strategy Recommendation

            5.2.3.   CNN based Emotion Recognition Model

            5.2.4.   Development of Performance Analyzer Model

5.3.      Summary

 

Part 6: Execution of Developed Intelligent Tutoring System

            6.1       Implementation of a System

            6.2       Learner Interface Model

            6.3       Domain Model

            6.4       Learner Model

            6.5       Pedagogy Model

                        6.5.1    Performance Analyzer model

            6.6       Learner Statistics

            6.7       Learner Feedback

            6.8       Summary

 

Part 7: Assessment of Developed Intelligent Tutoring System

7.1  Overview

7.2        Learner performance metrics

7.3  SeisTutor: a Comparative Analysis with Teachable, My-Moodle and

      Course-Builder Learning Management System

7.4         Summary

 

Part 8: Analysis & Metrics

            8.1       Critical analysis of Performance Metrics

            8.2       Statistical Analysis of Learner Engagement

            8.3       Learner Learning Analysis using Evaluation Model “Kirkpatrick”

            8.4       Future Scope

References

 

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O autorze

Dr. Ninni Singh obtained her Ph.D. in Computer Science and Engineering from the University of Petroleum and Energy Studies Dehradun Uttrakhand. She is currently working as Associate Professor in the Computer Science and Engineering Department at CMR Institute of Technology (Autonomous), Hyderabad. Dr. Singh has published many technical articles in refereed journals and international conferences. Her areas of interest are artificial intelligence, expert system, artificial neural network, cryptography and network security, distributed system, and wireless sensor and mesh network.

Dr. Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science & Engineering and Dean of Academic affairs at CMR Institute of Technology Hyderabad (Affiliated to Jawaharlal Nehru Technological University, Hyderabad). His research interests are in the areas of cyber security, ANN, image processing, and web technology.

Dr. Jacek M. Zurada is Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, Kentucky, USA, where he served as Department Chair and Distinguished University Scholar. He received his M.S. and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. He has published over 420 journal and conference papers in neural networks, deep learning, computational intelligence, data mining, image processing, and VLSI circuits. He has authored or co-authored three books, including the pioneering text Introduction to artificial neural systems, co-edited the volumes computational intelligence: imitating life, knowledge-based neurocomputing, and co-edited twenty volumes in Springer Lecture Notes on Computer Science. In addition to his pioneering neural networks textbook, his most recognized achievements include an extension of complex-valued neurons to associative memories and perception networks; sensitivity concepts applied to multilayer neural networks; application of networks to clustering, biomedical image classification, and drug dosing; blind sources separation; and rule extraction as a tool for prediction of protein secondary structure.

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