Singh / Gunjan / Zurada Cognitive Tutor

Custom-Tailored Pedagogical Approach
1. Auflage 2022
ISBN: 978-981-19-5197-8
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 180 Seiten, eBook

Reihe: Advanced Technologies and Societal Change

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.

Zielgruppe

Research

Autoren/Hrsg.

Singh, Ninni

Gunjan, Vinit Kumar

Zurada, Jacek M.

Inhaltsverzeichnis

Introduction.- Domain Modeling.- Pedagogy Modeling.- Building SeisTutor Intelligent Tutoring System for Experimental Learning Domain.- Pedagogy Modeling for Building SeisTutor Intelligent Tutoring System.- Execution of developed Intelligent Tutoring System.- Performance Metrics: Intelligent Tutoring System.- Analysis of Performance Metrics.

Über Autor(innen)

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 Electricaland 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.