Cognitive Tutor

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. ​

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.