{"product_id":"artificial-intelligence-and-iot-in-online-education-systems-monitoring-assessment-and-evaluation-hardback-9781394302635","title":"Artificial Intelligence and IoT in Online Education Systems; Monitoring, Assessment, and Evaluation (Hardback) 9781394302635","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence and IoT in Online Education Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMonitoring, Assessment, and Evaluation\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRamanujam E. (Edited by), Ramanujam (Author), Chandan Chakraborty (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394302635, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 12 December 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e560 pages\u003cbr\u003e28 x 19 x 2.5 cm, 0.998 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eDesign the future of digital education with this essential book that provides a comprehensive guide to leveraging AI and IoT to create dynamic, inclusive virtual learning environments and effectively implement advanced online proctoring solutions.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe rapid development of online learning environments and virtual classrooms, coupled with the need for scalable, personalized education systems, has positioned AI as a key enabler of modern education. The advent of these technologies promises to reshape how we deliver, monitor, assess, and evaluate online learning. This book explores these critical intersections of technology and education, emphasizing the potential of AI and IoT not only to optimize outcomes but also to create more dynamic, responsive, and inclusive virtual learning environments. Focusing on problems that can be solved through computer vision, video and audio streaming, class imbalance data, audio-to-text processes, multi-modal and bi-modal aspects, hand-written strokes, text similarity, biomedical ethics, and advancements in machine and deep learning algorithms, this book comprehensively explores the effectiveness of these technologies in online proctoring. This essential guide will equip educators, technologists, administrators, and policymakers with the knowledge and perspective necessary to leverage these technologies effectively. \u003c\/p\u003e\n\u003cp\u003eReaders will find the book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplores various AI tools and techniques adopted for online proctoring examination systems;\u003c\/li\u003e \u003cli\u003eCovers critical analytical aspects of AI-assisted systems;\u003c\/li\u003e \u003cli\u003eDescribes a variety of experiments leading to uni- and multi-modal systems and IoT-based architecture using computer vision, machine learning, and deep learning algorithms;\u003c\/li\u003e \u003cli\u003eDiscusses the quality assurance and psychological aspects to preserve ethics during  examinations.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eEducational researchers and policymakers, as well as computer scientists working in AI, machine learning, data science, deep learning, computer vision, and statistics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Introduction to AI Tools for Online Proctoring 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 AI Literacy and Online Proctoring: Educational Perspectives and Strategies 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrihana Vasishta, Gitanjaly Chhabra and Noosha Mehdian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 AI in Education — Theoretical Framework 6\u003c\/p\u003e \u003cp\u003e1.3 AI-Assisted Educational Practices 8\u003c\/p\u003e \u003cp\u003e1.3.1 Hyper Sentient Syllabus 8\u003c\/p\u003e \u003cp\u003e1.3.2 Role of AI in Redesigning Assessment Strategies 9\u003c\/p\u003e \u003cp\u003e1.3.3 Framework for Adopting and Implementing AI-Assisted Online Proctoring Systems 11\u003c\/p\u003e \u003cp\u003e1.3.4 Ethical Implications of AI in Online Proctoring 19\u003c\/p\u003e \u003cp\u003e1.4 Strengthening Teacher Preparation for AI Literacy in Higher Education Curricula 19\u003c\/p\u003e \u003cp\u003e1.4.1 Updating Educator’s Knowledge of AI Concepts 19\u003c\/p\u003e \u003cp\u003e1.4.2 Utilizing AI-Enhanced Technologies for Personalized Learning 20\u003c\/p\u003e \u003cp\u003e1.4.3 Integrating AI Literacy Education with the TPACK Framework 20\u003c\/p\u003e \u003cp\u003e1.5 Conclusion and Implications 21\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Next-Generation Online Education Integrating AI and IoT for Superior Management and Evaluation 27\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAniket Kumar, Rajesh Kumar, Akshay Kumar, Prashant D. Yelpale and Aman Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 28\u003c\/p\u003e \u003cp\u003e2.2 AI and IoT in Online Education Systems 31\u003c\/p\u003e \u003cp\u003e2.2.1 Overview 31\u003c\/p\u003e \u003cp\u003e2.2.2 Smart Online Education Model 33\u003c\/p\u003e \u003cp\u003e2.2.3 Smart Online Classroom 34\u003c\/p\u003e \u003cp\u003e2.2.4 Smart Online Labs 36\u003c\/p\u003e \u003cp\u003e2.2.5 Smart Online Tutoring 36\u003c\/p\u003e \u003cp\u003e2.2.6 Smart Simulation 37\u003c\/p\u003e \u003cp\u003e2.2.7 Smart Online Evaluation 38\u003c\/p\u003e \u003cp\u003e2.2.8 Smart Online Security and Content Adaptation 38\u003c\/p\u003e \u003cp\u003e2.2.9 Application and Infrastructure Levels 41\u003c\/p\u003e \u003cp\u003e2.3 Functional Structure of IoT System 41\u003c\/p\u003e \u003cp\u003e2.3.1 Online Exam Management 42\u003c\/p\u003e \u003cp\u003e2.3.2 Automated Correction of Exam Papers 43\u003c\/p\u003e \u003cp\u003e2.3.3 Student’s Performance Calculation 44\u003c\/p\u003e \u003cp\u003e2.4 Emerging Technologies in the Online Education System 45\u003c\/p\u003e \u003cp\u003e2.4.1 AI Technologies 45\u003c\/p\u003e \u003cp\u003e2.4.2 AR, VR 48\u003c\/p\u003e \u003cp\u003e2.4.3 Big Data Technology 49\u003c\/p\u003e \u003cp\u003e2.4.4 Robotics and IoT Labs 49\u003c\/p\u003e \u003cp\u003e2.4.5 Cloud Computing Technology 50\u003c\/p\u003e \u003cp\u003e2.4.6 Machine Learning Technology 50\u003c\/p\u003e \u003cp\u003e2.4.7 Deep Learning Technology 51\u003c\/p\u003e \u003cp\u003e2.4.8 IoT Technology 51\u003c\/p\u003e \u003cp\u003e2.4.9 5G Technology 52\u003c\/p\u003e \u003cp\u003e2.4.10 Learning Management System (LMS) 52\u003c\/p\u003e \u003cp\u003e2.5 Challenges of AI and IoT in Online Education System 54\u003c\/p\u003e \u003cp\u003e2.6 The Future Vision of AI and IoT in Online Education Systems 56\u003c\/p\u003e \u003cp\u003e2.6.1 Emerging Trends and Future Applications 57\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Ethics of Using AI Tools in Education 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Ethical Integrity in Educational Contexts 65\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eC. Santhiya, Ravi Prasath S., Suriya Navaneetha Krishnan K. and Kannappan R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 66\u003c\/p\u003e \u003cp\u003e3.2 Types and Methods of Fake Credentials 67\u003c\/p\u003e \u003cp\u003e3.2.1 Counterfeit Diploma and Degrees 67\u003c\/p\u003e \u003cp\u003e3.2.2 Fake Transcripts 68\u003c\/p\u003e \u003cp\u003e3.2.3 Misrepresentation of Professional Licenses and Certifications 68\u003c\/p\u003e \u003cp\u003e3.2.4 Online Credential Verification Scams 69\u003c\/p\u003e \u003cp\u003e3.2.5 Impersonation of Genuine Graduates 70\u003c\/p\u003e \u003cp\u003e3.2.6 Use of Photoshop and Graphic Design Software 71\u003c\/p\u003e \u003cp\u003e3.3 Consequences of Fake Credentials 72\u003c\/p\u003e \u003cp\u003e3.3.1 Legal Consequences 73\u003c\/p\u003e \u003cp\u003e3.3.2 Educational and Professional Consequences 75\u003c\/p\u003e \u003cp\u003e3.3.3 Financial Consequences 76\u003c\/p\u003e \u003cp\u003e3.3.4 Loss of Trust 77\u003c\/p\u003e \u003cp\u003e3.3.5 Long-Term Implications 80\u003c\/p\u003e \u003cp\u003e3.3.6 Ethical and Psychological Consequences 81\u003c\/p\u003e \u003cp\u003e3.3.7 Public Shame 83\u003c\/p\u003e \u003cp\u003e3.4 Challenges in Detecting and Verifying Fake Credentials 84\u003c\/p\u003e \u003cp\u003e3.5 Role of Technology in Facilitating and Combating Fake Credentials 86\u003c\/p\u003e \u003cp\u003e3.6 Impact on Organizational Reputation and Public Trust 90\u003c\/p\u003e \u003cp\u003e3.7 Multi-Layered Approach to Tackling the Problem 91\u003c\/p\u003e \u003cp\u003e3.8 Innovative Solutions and Technologies 94\u003c\/p\u003e \u003cp\u003e3.9 Promoting Awareness and Education 96\u003c\/p\u003e \u003cp\u003e3.10 Future Trends and Strategies 99\u003c\/p\u003e \u003cp\u003e3.11 Conclusion 99\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Psychological and Ethical Aspects of Using Intelligent Systems in Online Proctoring 103\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMukesh Chaware and Sreejith Alathur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 104\u003c\/p\u003e \u003cp\u003e4.1.1 Importance of Proctoring in Online Examination 107\u003c\/p\u003e \u003cp\u003e4.1.2 Briefing on Intelligent Systems (AI, BD, IoT) 108\u003c\/p\u003e \u003cp\u003e4.1.3 Relevance of Intelligent Systems to Online Proctoring 111\u003c\/p\u003e \u003cp\u003e4.2 The Advent of AI in Online Proctoring 112\u003c\/p\u003e \u003cp\u003e4.2.1 The Need for AI in Online Proctoring 112\u003c\/p\u003e \u003cp\u003e4.2.2 Evolution and Current State of AI Applications in Online Proctoring 114\u003c\/p\u003e \u003cp\u003e4.3 The Prevailing Situation 116\u003c\/p\u003e \u003cp\u003e4.4 Psychological Aspects 119\u003c\/p\u003e \u003cp\u003e4.4.1 User Perceptions of AI-Driven Proctoring 119\u003c\/p\u003e \u003cp\u003e4.4.2 Impact on Test Taker Stress and Performance 121\u003c\/p\u003e \u003cp\u003e4.4.3 Privacy Concerns and their Psychological Implications 122\u003c\/p\u003e \u003cp\u003e4.5 Ethical Aspects 124\u003c\/p\u003e \u003cp\u003e4.5.1 Ethical Implications of Using AI for Surveillance 125\u003c\/p\u003e \u003cp\u003e4.5.2 Potential for Bias and Discrimination in AI Proctoring 126\u003c\/p\u003e \u003cp\u003e4.6 Discussion and Recommendations 127\u003c\/p\u003e \u003cp\u003e4.6.1 Strategies for Ethically Implementing AI in Online Proctoring 127\u003c\/p\u003e \u003cp\u003e4.6.2 Recommendations for Addressing Psychological Concerns 128\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 128\u003c\/p\u003e \u003cp\u003eAcknowledgments 131\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: State-of-the-Art AI Tools and Techniques for Online Proctoring 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Comprehensive Review of Deep Learning Models on Detecting Student Emotions in Online Education 139\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThangavel Murugan, A.M. Abirami and P. Karthikeyan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 140\u003c\/p\u003e \u003cp\u003e5.1.1 Research Overview 140\u003c\/p\u003e \u003cp\u003e5.1.2 Importance of Detecting Student Emotions in Online Education 141\u003c\/p\u003e \u003cp\u003e5.1.3 Purpose of the Literature Review 141\u003c\/p\u003e \u003cp\u003e5.2 Understanding Student Emotions 142\u003c\/p\u003e \u003cp\u003e5.2.1 Definition of Emotions 142\u003c\/p\u003e \u003cp\u003e5.2.2 The Role of Emotions in Learning 142\u003c\/p\u003e \u003cp\u003e5.2.3 Significance of Detecting Student Emotions in Online Education 143\u003c\/p\u003e \u003cp\u003e5.3 Overview of Deep Learning 143\u003c\/p\u003e \u003cp\u003e5.3.1 Definition of Deep Learning 143\u003c\/p\u003e \u003cp\u003e5.3.2 Benefits of Deep Learning in Educational Research 143\u003c\/p\u003e \u003cp\u003e5.3.3 Applications of Deep Learning in Detecting Emotions 144\u003c\/p\u003e \u003cp\u003e5.4 Literature Review 144\u003c\/p\u003e \u003cp\u003e5.4.1 Studies on Detecting Student Emotions in Online Education 145\u003c\/p\u003e \u003cp\u003e5.4.1.1 Methods Used for Emotion Detection 146\u003c\/p\u003e \u003cp\u003e5.4.1.2 Effectiveness of Different Approaches 147\u003c\/p\u003e \u003cp\u003e5.4.2 Applications of Deep Learning in Emotion Detection 148\u003c\/p\u003e \u003cp\u003e5.4.2.1 Algorithms Used in Deep Learning for Emotion Recognition 149\u003c\/p\u003e \u003cp\u003e5.4.2.2 Success Stories and Challenges Faced in Using Deep Learning 150\u003c\/p\u003e \u003cp\u003e5.4.2.3 Proposed Model for Student Behavior Analysis in Classroom 151\u003c\/p\u003e \u003cp\u003e5.4.2.4 Literature Summary and Analysis 153\u003c\/p\u003e \u003cp\u003e5.5 Challenges in Detecting Student Emotions 153\u003c\/p\u003e \u003cp\u003e5.5.1 Technical Challenges 156\u003c\/p\u003e \u003cp\u003e5.5.1.1 Data Collection and Processing 156\u003c\/p\u003e \u003cp\u003e5.5.1.2 Model Accuracy and Reliability 156\u003c\/p\u003e \u003cp\u003e5.5.2 Ethical Considerations 157\u003c\/p\u003e \u003cp\u003e5.5.2.1 Privacy Concerns 157\u003c\/p\u003e \u003cp\u003e5.5.2.2 Bias in Emotion Detection Algorithms 157\u003c\/p\u003e \u003cp\u003e5.6 Future Directions and Recommendations 158\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 159\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Deep Learning Models for Monitoring Student’s Emotion During the Class: A Comprehensive Survey 165\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVamshi Krishna B., N. Padmavathy and Ajeet Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 166\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 168\u003c\/p\u003e \u003cp\u003e6.2.1 Deep Learning Approach 169\u003c\/p\u003e \u003cp\u003e6.2.2 Transfer Learning 171\u003c\/p\u003e \u003cp\u003e6.3 Research Background 178\u003c\/p\u003e \u003cp\u003e6.3.1 Computer Vision 178\u003c\/p\u003e \u003cp\u003e6.3.2 Internet of Things (IoT) 181\u003c\/p\u003e \u003cp\u003e6.3.3 Deep Learning Architectures 183\u003c\/p\u003e \u003cp\u003e6.3.3.1 ConvNet 184\u003c\/p\u003e \u003cp\u003e6.3.3.2 Recurrent Neural Network 186\u003c\/p\u003e \u003cp\u003e6.3.4 Pre-Trained Models 189\u003c\/p\u003e \u003cp\u003e6.4 Prediction Models for Tracking and Monitoring Students 193\u003c\/p\u003e \u003cp\u003e6.4.1 Emotion Recognition Models 194\u003c\/p\u003e \u003cp\u003e6.4.2 Learning Engagement Models 196\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 197\u003c\/p\u003e \u003cp\u003eReferences 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Comparative Analysis of Head Pose Estimation and Eye Gaze Tracking with Machine Learning Classifiers for Proctored Online Examination 203\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajarajeswari P., Shivagangatharani B. and Karthikeyan Jothikumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 204\u003c\/p\u003e \u003cp\u003e7.1.1 Head Pose Estimation 204\u003c\/p\u003e \u003cp\u003e7.1.2 Eye Gaze Tracking 204\u003c\/p\u003e \u003cp\u003e7.1.3 Relevance of Head Pose Estimation and Eye Gaze Tracking in Online Proctored Exams 206\u003c\/p\u003e \u003cp\u003e7.2 Benchmark Datasets for Head Pose and Eye Gaze Tracking 207\u003c\/p\u003e \u003cp\u003e7.3 Apparatus for Estimating Head Pose and Tracking Eye Gaze 214\u003c\/p\u003e \u003cp\u003e7.4 Models for Head Pose Estimation and Eye Gaze Tracking 217\u003c\/p\u003e \u003cp\u003e7.4.1 Geometrical Method Based on Interest Points 217\u003c\/p\u003e \u003cp\u003e7.4.2 Gradient Boosting Regression 218\u003c\/p\u003e \u003cp\u003e7.4.3 Genetic Algorithm 219\u003c\/p\u003e \u003cp\u003e7.4.4 Linear Discriminant Analysis (LDA) and Discrete Wavelet Transform (DWT) 220\u003c\/p\u003e \u003cp\u003e7.4.5 Aff Net 221\u003c\/p\u003e \u003cp\u003e7.4.6 FSA-Net 223\u003c\/p\u003e \u003cp\u003e7.4.7 Multi-Modal Convolutional Neural Network 223\u003c\/p\u003e \u003cp\u003e7.5 Comparison of Models for Head Pose Estimation and Eye Gaze Tracking 224\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 226\u003c\/p\u003e \u003cp\u003eReferences 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Uni- and Multi-Modal Aspects in the Online Proctoring System: Survey 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDiana Moses and Dainty M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 232\u003c\/p\u003e \u003cp\u003e8.1.1 Online Proctoring Techniques 235\u003c\/p\u003e \u003cp\u003e8.1.2 Concerns in Online Proctoring Systems 237\u003c\/p\u003e \u003cp\u003e8.2 AI-Based Online Proctoring System 240\u003c\/p\u003e \u003cp\u003e8.2.1 Online Proctoring Process 240\u003c\/p\u003e \u003cp\u003e8.2.1.1 Proctoring Prior to Examination 241\u003c\/p\u003e \u003cp\u003e8.2.1.2 Proctoring During Examination 243\u003c\/p\u003e \u003cp\u003e8.2.1.2(a) Examinee Behavior Screening 244\u003c\/p\u003e \u003cp\u003e8.2.1.2(b) Examinee System Screening 247\u003c\/p\u003e \u003cp\u003e8.2.1.2(c) Examinee Environment Screening 248\u003c\/p\u003e \u003cp\u003e8.3 Existing AI-Based Online Proctoring Frameworks 249\u003c\/p\u003e \u003cp\u003e8.4 Challenges in AI-Based Online Proctoring Frameworks 253\u003c\/p\u003e \u003cp\u003e8.5 Future Scope of AI-Based Proctoring Frameworks 256\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 259\u003c\/p\u003e \u003cp\u003eReferences 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Advancing Academic Integrity: AI and IoT in Enhancing Monitoring for Online Examination Systems 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Shanthalakshmi Revathy and J. Mangaiyarkkarasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 266\u003c\/p\u003e \u003cp\u003e9.2 Predictive Analysis of Student Performance 267\u003c\/p\u003e \u003cp\u003e9.2.1 Data Collection 269\u003c\/p\u003e \u003cp\u003e9.2.2 Data Preprocessing 269\u003c\/p\u003e \u003cp\u003e9.2.3 Feature Engineering 270\u003c\/p\u003e \u003cp\u003e9.2.4 Model Selection and Training 270\u003c\/p\u003e \u003cp\u003e9.2.5 Model Evaluation 270\u003c\/p\u003e \u003cp\u003e9.2.6 Model Deployment 271\u003c\/p\u003e \u003cp\u003e9.3 Authentication of Students 271\u003c\/p\u003e \u003cp\u003e9.4 Supervision of Examination 272\u003c\/p\u003e \u003cp\u003e9.4.1 Plagiarism Detection 273\u003c\/p\u003e \u003cp\u003e9.4.2 Fraud Detection and Malpractice Prevention 275\u003c\/p\u003e \u003cp\u003e9.4.3 Multiple Account Detection 275\u003c\/p\u003e \u003cp\u003e9.4.4 E-Cheating Intelligence Agents 276\u003c\/p\u003e \u003cp\u003e9.4.5 Detection of Liveliness Spoofs 277\u003c\/p\u003e \u003cp\u003e9.4.6 Anomaly Detection 277\u003c\/p\u003e \u003cp\u003e9.5 Challenges in Monitoring 279\u003c\/p\u003e \u003cp\u003e9.5.1 Privacy Concerns 281\u003c\/p\u003e \u003cp\u003e9.5.2 Security Challenges 281\u003c\/p\u003e \u003cp\u003e9.5.3 Fairness Consideration 282\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 283\u003c\/p\u003e \u003cp\u003eReferences 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimizing Academic Excellence: Leveraging Advanced AI Tools for Assessment and Evaluation in Modern Online Examination Systems 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManikandakumar M., Karthikeyan P., Senthamarai Kannan K., Arul V. and Vigneshwaran T.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 288\u003c\/p\u003e \u003cp\u003e10.2 Role of AI in Online Examination Systems 289\u003c\/p\u003e \u003cp\u003e10.2.1 Benefits of AI in Assessments 290\u003c\/p\u003e \u003cp\u003e10.2.2 Personalization of Assessments 290\u003c\/p\u003e \u003cp\u003e10.2.3 Efficiency and Time-Saving 291\u003c\/p\u003e \u003cp\u003e10.2.4 Fairness and Objectivity 291\u003c\/p\u003e \u003cp\u003e10.2.5 Scalability and Accessibility 291\u003c\/p\u003e \u003cp\u003e10.2.6 Enhanced Security and Integrity 291\u003c\/p\u003e \u003cp\u003e10.2.7 Data-Driven Insights 292\u003c\/p\u003e \u003cp\u003e10.2.8 Continuous Learning and Improvement 292\u003c\/p\u003e \u003cp\u003e10.3 Advanced AI Tools for Assessment 293\u003c\/p\u003e \u003cp\u003e10.3.1 Knewton 293\u003c\/p\u003e \u003cp\u003e10.3.2 DreamBox 295\u003c\/p\u003e \u003cp\u003e10.3.3 Edpuzzle 297\u003c\/p\u003e \u003cp\u003e10.3.4 Squirrel AI 300\u003c\/p\u003e \u003cp\u003e10.3.5 ProctorU 301\u003c\/p\u003e \u003cp\u003e10.3.6 Smart Sparrow 303\u003c\/p\u003e \u003cp\u003e10.3.7 MoodleNet 304\u003c\/p\u003e \u003cp\u003e10.3.8 Canvas by Instructure 306\u003c\/p\u003e \u003cp\u003e10.4 Implementing AI Tools in Online Examination Systems 308\u003c\/p\u003e \u003cp\u003e10.4.1 Needs for AI in Online Examination Systems 308\u003c\/p\u003e \u003cp\u003e10.4.2 Steps for Implementing AI Tools 309\u003c\/p\u003e \u003cp\u003e10.4.3 Advantages of AI in Online Examinations 309\u003c\/p\u003e \u003cp\u003e10.4.4 Challenges of Implementing AI 310\u003c\/p\u003e \u003cp\u003e10.5 Future Trends 310\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 311\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Case Studies: AI and IoT in Education, Online Proctoring 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Evaluation of Web Design Deficiency and Anxiety Constructs, with Computer‐Based Test: Use Case in India 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJuby Thomas, Ashique Ali K.A., Vishnu Achutha Menon, Sateesh Kumar T.K. and Lijo P. Thomas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 318\u003c\/p\u003e \u003cp\u003e11.2 Review of Literature 319\u003c\/p\u003e \u003cp\u003e11.3 Methodology 325\u003c\/p\u003e \u003cp\u003e11.4 Results 328\u003c\/p\u003e \u003cp\u003e11.4.1 Structural Model 331\u003c\/p\u003e \u003cp\u003e11.5 Discussions 335\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 338\u003c\/p\u003e \u003cp\u003eAcknowledgment 339\u003c\/p\u003e \u003cp\u003eReferences 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 AI for Learners’ Emotions — A Perspective Approach of Analysis During Online Assessments 343\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS.J. Sheeba Sharon, R. Mary Sophia Chitra and C. Santhiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 344\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 346\u003c\/p\u003e \u003cp\u003e12.3 Role of Emotions in Learning 349\u003c\/p\u003e \u003cp\u003e12.4 Challenges in Online Assessments 350\u003c\/p\u003e \u003cp\u003e12.5 The Rise of AI in Education 351\u003c\/p\u003e \u003cp\u003e12.6 AI Tools for Monitoring Learner Emotions 351\u003c\/p\u003e \u003cp\u003e12.6.1 Facial Expression Analysis Tools 351\u003c\/p\u003e \u003cp\u003e12.6.2 Voice Analysis Tools 352\u003c\/p\u003e \u003cp\u003e12.6.3 Sentiment Analysis and NLP Tools 352\u003c\/p\u003e \u003cp\u003e12.6.4 Physiological Monitoring Tools 352\u003c\/p\u003e \u003cp\u003e12.7 Methodology 353\u003c\/p\u003e \u003cp\u003e12.7.1 Selection of Appropriate Tools 354\u003c\/p\u003e \u003cp\u003e12.7.2 Data Collection and Consent 354\u003c\/p\u003e \u003cp\u003e12.7.3 Integration with Assessment Platforms 354\u003c\/p\u003e \u003cp\u003e12.7.4 Training for Educators and Administrators 355\u003c\/p\u003e \u003cp\u003e12.7.5 Pilot Testing and Evaluation 355\u003c\/p\u003e \u003cp\u003e12.7.6 Full Implementation and Ongoing Monitoring 355\u003c\/p\u003e \u003cp\u003e12.7.7 Addressing Ethical and Privacy Concerns 355\u003c\/p\u003e \u003cp\u003e12.7.8 Feedback and Continuous Improvement 356\u003c\/p\u003e \u003cp\u003e12.8 Advantages of Using AI Tools 356\u003c\/p\u003e \u003cp\u003e12.9 Possible Implementational Risks 357\u003c\/p\u003e \u003cp\u003e12.10 Demerits and Future Scope 358\u003c\/p\u003e \u003cp\u003e12.11 Conclusion 359\u003c\/p\u003e \u003cp\u003eReferences 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Implementing Personalized Adaptive Online Assessments through Deep Learning 365\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFawad Naseer, Noreen Sattar, Akhtar Rasool, Kamel Jebreen and Usman Khalid\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 366\u003c\/p\u003e \u003cp\u003e13.1.1 The Need for Adaptive Assessment Systems 366\u003c\/p\u003e \u003cp\u003e13.1.2 The Role of DL in Education 367\u003c\/p\u003e \u003cp\u003e13.1.3 Research Context and Case Studies 367\u003c\/p\u003e \u003cp\u003e13.2 Literature Review 368\u003c\/p\u003e \u003cp\u003e13.3 Methodology 370\u003c\/p\u003e \u003cp\u003e13.3.1 Description of the DL Algorithms and Models 370\u003c\/p\u003e \u003cp\u003e13.3.1.1 Convolutional Neural Networks (CNNs) 371\u003c\/p\u003e \u003cp\u003e13.3.1.2 Recurrent Neural Networks (RNNs) 371\u003c\/p\u003e \u003cp\u003e13.3.1.3 Long Short-Term Memory (LSTM) Networks 372\u003c\/p\u003e \u003cp\u003e13.3.2 Data Collection and Pre-Processing Methods 373\u003c\/p\u003e \u003cp\u003e13.3.2.1 Data Collection 373\u003c\/p\u003e \u003cp\u003e13.3.2.2 Data Pre-Processing 374\u003c\/p\u003e \u003cp\u003e13.3.3 Steps Involved in Developing and Implementing the Adaptive Assessment System 375\u003c\/p\u003e \u003cp\u003e13.3.3.1 Model Design and Training 375\u003c\/p\u003e \u003cp\u003e13.3.3.2 Adaptive Assessment Generation 376\u003c\/p\u003e \u003cp\u003e13.3.3.3 Real-Time Feedback System 376\u003c\/p\u003e \u003cp\u003e13.3.3.4 Implementation and Testing 377\u003c\/p\u003e \u003cp\u003e13.4 Case Studies 378\u003c\/p\u003e \u003cp\u003e13.4.1 Case Study 1: Beaconhouse International College (bic) 378\u003c\/p\u003e \u003cp\u003e13.4.1.1 Background and Context 378\u003c\/p\u003e \u003cp\u003e13.4.1.2 Implementation Process 378\u003c\/p\u003e \u003cp\u003e13.4.1.3 Key Findings 380\u003c\/p\u003e \u003cp\u003e13.4.1.4 Challenges and Solutions 381\u003c\/p\u003e \u003cp\u003e13.4.2 Case Study 2: Government College University Faisalabad (GCUF) 381\u003c\/p\u003e \u003cp\u003e13.4.2.1 Background and Context 381\u003c\/p\u003e \u003cp\u003e13.4.2.2 Implementation Process 382\u003c\/p\u003e \u003cp\u003e13.4.2.3 Key Findings 382\u003c\/p\u003e \u003cp\u003e13.4.2.4 Challenges and Solutions 383\u003c\/p\u003e \u003cp\u003e13.5 Results and Discussion 384\u003c\/p\u003e \u003cp\u003e13.5.1 Improvement in Learning Outcomes 384\u003c\/p\u003e \u003cp\u003e13.5.2 Increase in Engagement Rates 386\u003c\/p\u003e \u003cp\u003e13.5.3 Reduction in Exam-Related Anxiety 388\u003c\/p\u003e \u003cp\u003e13.5.4 Enhanced Overall Performance 390\u003c\/p\u003e \u003cp\u003e13.5.5 Comparative Analysis of the Case Studies 392\u003c\/p\u003e \u003cp\u003e13.5.5.1 Similarities 392\u003c\/p\u003e \u003cp\u003e13.5.5.2 Differences 392\u003c\/p\u003e \u003cp\u003e13.5.6 Future Research Directions 394\u003c\/p\u003e \u003cp\u003e13.5.7 Limitations of the Study 395\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 395\u003c\/p\u003e \u003cp\u003eReferences 396\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Generative Artificial Intelligence for Online Education Systems 399\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMunmi Dutta and Vinay Kumar Goyal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 400\u003c\/p\u003e \u003cp\u003e14.2 The Types of GAI Models 401\u003c\/p\u003e \u003cp\u003e14.3 Working of GAI 401\u003c\/p\u003e \u003cp\u003e14.3.1 Generative Modeling 402\u003c\/p\u003e \u003cp\u003e14.3.2 GANs 403\u003c\/p\u003e \u003cp\u003e14.3.3 Transformer-Based Models 404\u003c\/p\u003e \u003cp\u003e14.4 Use Cases of GAI 406\u003c\/p\u003e \u003cp\u003e14.5 The Limitations of GAI 407\u003c\/p\u003e \u003cp\u003e14.6 Adaptive Learning Platforms 408\u003c\/p\u003e \u003cp\u003e14.7 GAI and Adaptive Learning Intersection 409\u003c\/p\u003e \u003cp\u003e14.7.1 Potential Benefits of Integrating GAI and Adaptive Learning 409\u003c\/p\u003e \u003cp\u003e14.7.2 Some Examples of Successful Integration 409\u003c\/p\u003e \u003cp\u003e14.7.3 Future Trends of GAI and Adaptive Learning 410\u003c\/p\u003e \u003cp\u003e14.7.4 Prospective Developments in GAI for the Education Sector 411\u003c\/p\u003e \u003cp\u003e14.8 Implications for Educators and Learners 412\u003c\/p\u003e \u003cp\u003e14.9 GAI Effect on Workforce 412\u003c\/p\u003e \u003cp\u003e14.10 GAI Has Already Transformed Education 413\u003c\/p\u003e \u003cp\u003e14.11 Effect on the Participation and Performance of Learners 414\u003c\/p\u003e \u003cp\u003e14.11.1 Develop Their Expressiveness and Creativity 414\u003c\/p\u003e \u003cp\u003e14.11.2 Develop Their Information Literacy and Research Abilities 414\u003c\/p\u003e \u003cp\u003e14.11.3 Improve Their Capacity for Self-Control and Metacognition 415\u003c\/p\u003e \u003cp\u003e14.12 The Education Sector’s Challenges with GAI 415\u003c\/p\u003e \u003cp\u003e14.12.1 Challenge Cause Due to Plagiarism 415\u003c\/p\u003e \u003cp\u003e14.12.2 Equity 415\u003c\/p\u003e \u003cp\u003e14.12.3 Privacy 416\u003c\/p\u003e \u003cp\u003e14.12.4 Efficacy 416\u003c\/p\u003e \u003cp\u003e14.12.5 Detection 417\u003c\/p\u003e \u003cp\u003e14.12.6 Appropriate Use 417\u003c\/p\u003e \u003cp\u003e14.12.7 Authorship 417\u003c\/p\u003e \u003cp\u003e14.13 Policymakers and Educators Need to Reconsider the Current Educational Paradigm 418\u003c\/p\u003e \u003cp\u003e14.14 Access and Equity Comes First 418\u003c\/p\u003e \u003cp\u003e14.15 United Nations Educational, Scientific and Cultural Organization’s (UNESCO’s) Policy for Reshaping Education by Using GAI 419\u003c\/p\u003e \u003cp\u003e14.16 Conclusion 420\u003c\/p\u003e \u003cp\u003eReferences 420\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Level of Academic Misconduct During Online Unproctored Examination with Perception of Engineering Students in India 423\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Sasikala, G. Vidyasree, C. Selvan and R. Ragunath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 424\u003c\/p\u003e \u003cp\u003e15.2 Literature Review 425\u003c\/p\u003e \u003cp\u003e15.3 Research Methodology 428\u003c\/p\u003e \u003cp\u003e15.3.1 Sampling 430\u003c\/p\u003e \u003cp\u003e15.3.2 Data Analysis and Findings 432\u003c\/p\u003e \u003cp\u003e15.3.3 Relative Importance 436\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 439\u003c\/p\u003e \u003cp\u003eReferences 439\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Student Activity Monitoring Using Hybrid Deep Learning Technique During Online Examinations 443\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDevi Naveen, Akshitha Katkeri, Manikantha K., A.K. Sreeja and Satish Kumar V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 444\u003c\/p\u003e \u003cp\u003e16.1.1 Motivations 445\u003c\/p\u003e \u003cp\u003e16.1.2 Objective and Design 446\u003c\/p\u003e \u003cp\u003e16.1.3 Contributions 447\u003c\/p\u003e \u003cp\u003e16.2 Related Works 447\u003c\/p\u003e \u003cp\u003e16.2.1 Image Information Systems (IIS) 447\u003c\/p\u003e \u003cp\u003e16.2.2 Multi-Modal System (MMS) 449\u003c\/p\u003e \u003cp\u003e16.2.3 Behavior-Based Analysis 450\u003c\/p\u003e \u003cp\u003e16.3 Methodology — The Theoretical Foundation of the Proposed Model 451\u003c\/p\u003e \u003cp\u003e16.3.1 Dataset Collection 451\u003c\/p\u003e \u003cp\u003e16.4 Experimental Results and Discussion 456\u003c\/p\u003e \u003cp\u003e16.5 Conclusion and Future Work 460\u003c\/p\u003e \u003cp\u003eReferences 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Multicue Facial Emotion Expression Using Lightweight Deep Learning Models 465\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Hemaswathi, P. Rajkumar, N. Mohan Prabhu and R. Dhivya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 466\u003c\/p\u003e \u003cp\u003e17.1.1 Types of Facial Expression and Its Features 467\u003c\/p\u003e \u003cp\u003e17.2 Related Works 469\u003c\/p\u003e \u003cp\u003e17.3 Materials and Method 472\u003c\/p\u003e \u003cp\u003e17.3.1 Face and Facial Landmark Detection 474\u003c\/p\u003e \u003cp\u003e17.3.2 Convolution Neural Network (ConvNEt) Architecture 475\u003c\/p\u003e \u003cp\u003e17.3.3 VGG-16 Architecture 476\u003c\/p\u003e \u003cp\u003e17.3.4 InceptionV3 Architecture 477\u003c\/p\u003e \u003cp\u003e17.3.5 ResNet 50 477\u003c\/p\u003e \u003cp\u003e17.4 Experimental Result Analysis 478\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 482\u003c\/p\u003e \u003cp\u003eReferences 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Challenges and Future Scope of AI in Online Proctoring 485\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Machine-Learning-Based Online Assessment of Students’ Academic Performance in Moodle Learning Management System 487\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eReshma V.K., Nisha A.K., Radhika K. Manjusha, Divya P. and Sundaraselvan S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 488\u003c\/p\u003e \u003cp\u003e18.2 Literature Review 491\u003c\/p\u003e \u003cp\u003e18.3 Research Methodology 493\u003c\/p\u003e \u003cp\u003e18.3.1 Dataset Acquisition 494\u003c\/p\u003e \u003cp\u003e18.3.2 Dataset Pre-Processing 495\u003c\/p\u003e \u003cp\u003e18.3.3 Data Analysis 495\u003c\/p\u003e \u003cp\u003e18.3.4 Linear Regression 496\u003c\/p\u003e \u003cp\u003e18.3.5 Correlation 496\u003c\/p\u003e \u003cp\u003e18.3.6 Multiple Regression 496\u003c\/p\u003e \u003cp\u003e18.3.7 Lasso Regression 496\u003c\/p\u003e \u003cp\u003e18.4 Results and Discussion 496\u003c\/p\u003e \u003cp\u003e18.4.1 Correlation 497\u003c\/p\u003e \u003cp\u003e18.4.2 Scatter Plot 498\u003c\/p\u003e \u003cp\u003e18.4.3 Linear Regression 500\u003c\/p\u003e \u003cp\u003e18.4.4 Multiple Linear Regression 504\u003c\/p\u003e \u003cp\u003e18.4.5 Lasso Regression 508\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 509\u003c\/p\u003e \u003cp\u003e18.6 Future Research 510\u003c\/p\u003e \u003cp\u003eReferences 511\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Issues and Challenges of Using Artificial Intelligence Proctoring Tools 515\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Senthil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 515\u003c\/p\u003e \u003cp\u003e19.2 Literature Review 517\u003c\/p\u003e \u003cp\u003e19.2.1 Features of AI-Based Online Proctoring Tools 520\u003c\/p\u003e \u003cp\u003e19.3 Issues and Challenges of Using AI Proctoring Tools 522\u003c\/p\u003e \u003cp\u003e19.4 Case Study 524\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 527\u003c\/p\u003e \u003cp\u003eReferences 529\u003c\/p\u003e \u003cp\u003eIndex 533\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433519608088,"sku":"9781394302635","price":182.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394302635.jpg?v=1784853426","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-and-iot-in-online-education-systems-monitoring-assessment-and-evaluation-hardback-9781394302635","provider":"Freshly Printed Books","version":"1.0","type":"link"}