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This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis. Although these three technologies have been widely used by researchers around the globe by academic disciplines and by R&D departments in the IT industry, they have not yet been used extensively for the purposes of education. The authors present a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content and learning resources based on the needs and preferences of individual learners. According to reports, in 2018 the vast majority of internet users worldwide are active on social networks, and the global average social network penetration rate as of 2018 is close to half the population. Employing social networking technologies in the field of education allows the latest technological advances to be used to create interactive educational environments where students can learn, collaborate with peers and communicate with tutors while benefiting from a social and pedagogical structure similar to a real class. The book first discusses in detail the current trend of social networking-based learning. It then provides a novel framework that moves further away from digital learning technologies while incorporating a wide range of recent advances to provide solutions to future challenges. This approach incorporates machine learning to the student-modeling component, which also uses conceptual frameworks and pedagogical theories in order to further promote individualization and adaptivity in e-learning environments. Moreover, it examines error diagnosis, misconceptions, tailored testing and collaboration between students are examined and proposes new approaches for these modules. Sentiment analysis is also incorporated into the general framework, supporting personalized learning by considering the user’s emotional state, and creating a user-friendly learning environment tailored to students’ needs. Support for students, in the form of motivation, completes the framework. This book helps researchers in the field of knowledge-based software engineering to build more sophisticated personalized educational software, while retaining a high level of adaptivity and user-friendliness within human–computer interactions. Furthermore, it is a valuable resource for educators and software developers designing and implementing intelligent tutoring systems and adaptive educational hypermedia systems. .

Artificial intelligence --- Education --- Educational applications. --- Data processing --- Computational intelligence. --- Artificial intelligence. --- Educational technology. --- Computational Intelligence. --- Artificial Intelligence. --- Technology and Digital Education. --- Instructional technology --- Technology in education --- Technology --- Educational innovations --- Instructional systems --- Teaching --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Intelligence, Computational --- Soft computing --- Aids and devices

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This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis. Although these three technologies have been widely used by researchers around the globe by academic disciplines and by R&D departments in the IT industry, they have not yet been used extensively for the purposes of education. The authors present a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content and learning resources based on the needs and preferences of individual learners. According to reports, in 2018 the vast majority of internet users worldwide are active on social networks, and the global average social network penetration rate as of 2018 is close to half the population. Employing social networking technologies in the field of education allows the latest technological advances to be used to create interactive educational environments where students can learn, collaborate with peers and communicate with tutors while benefiting from a social and pedagogical structure similar to a real class. The book first discusses in detail the current trend of social networking-based learning. It then provides a novel framework that moves further away from digital learning technologies while incorporating a wide range of recent advances to provide solutions to future challenges. This approach incorporates machine learning to the student-modeling component, which also uses conceptual frameworks and pedagogical theories in order to further promote individualization and adaptivity in e-learning environments. Moreover, it examines error diagnosis, misconceptions, tailored testing and collaboration between students are examined and proposes new approaches for these modules. Sentiment analysis is also incorporated into the general framework, supporting personalized learning by considering the user’s emotional state, and creating a user-friendly learning environment tailored to students’ needs. Support for students, in the form of motivation, completes the framework. This book helps researchers in the field of knowledge-based software engineering to build more sophisticated personalized educational software, while retaining a high level of adaptivity and user-friendliness within human–computer interactions. Furthermore, it is a valuable resource for educators and software developers designing and implementing intelligent tutoring systems and adaptive educational hypermedia systems. .

Computer assisted instruction --- Audiovisual methods --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- onderwijstechnologie --- computerondersteund onderwijs --- KI (kunstmatige intelligentie)

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Intro -- Title Page -- Foreword -- Contents -- Chapter 1: Introduction -- Chapter 2: Intelligent Knowledge-Based Approaches for Adaptive and Personalized Learning Technology Systems -- Chapter 3: Comparison of Techniques and Good Practices for Adaptive and Personalized Learning Technology Systems -- Chapter 4: Evaluation Methods of Intelligent Knowledge-Based Learning Technology Systems -- Chapter 5: Conclusions -- References.

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This volume constitutes the proceedings of the 17th International Conference on Intelligent Tutoring Systems, ITS 2021, held in Athens, Greece, in June 2021. Due to COVID-19 pandemic the conference was held virtually. The 22 full papers, 22 short papers and 18 other papers presented in this volume were carefully reviewed and selected from 87 submissions. Conforming to the current move of education, work and leisure online, the title of ITS 2021 was "Intelligent Tutoring Systems in an online world". Its objective was to present academic and research achievements of computer and cognitive sciences, artificial intelligence, and, due to its recent emergence, specifically, deep learning in tutoring and education.

Social sciences (general) --- Computer assisted instruction --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- informatica --- sociale wetenschappen --- computerondersteund onderwijs --- KI (kunstmatige intelligentie)

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Computer assisted instruction --- Audiovisual methods --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- onderwijstechnologie --- computerondersteund onderwijs --- KI (kunstmatige intelligentie)