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.

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.

This book constitutes the refereed proceedings of the 19th International Conference on Augmented Intelligence and Intelligent Tutoring Systems, ITS 2023, held in Corfu, Greece, during June 2-5, 2023. The 41 full papers and 19 short papers presented in this book were carefully reviewed and selected from 84 submissions. The papers are divided into the following topical sections: augmented intelligence in tutoring systems; augmented intelligence in healthcare informatics; augmented intelligence in games, serious games and virtual reality; neural networks and data mining; augmented intelligence and metaverse; security, privacy and ethics in augmented intelligence; and applied natural language processing.

This book summarizes the research findings presented at the 2nd International Conference on Novel & Intelligent Digital Systems (NiDS 2022). NiDS 2022 was implemented virtually due to COVID-19 restrictions, on September 29-30, 2022, under the auspices of the Institute of Intelligent Systems. NiDS lays special emphasis on the novelties of intelligent systems and on the interdisciplinary research which enables, supports, and enhances artificial intelligence (AI) in software development. It promotes high-quality research, creating a forum for the exploration of challenges and new advances in AI, and addresses experts, researchers, and scholars in the fields of artificial and computational intelligence in systems and in computer sciences in general, enabling them to learn more about pertinent, strongly related, and mutually complementary fields. The conference promotes an exchange of ideas, reinforcing and expanding the network of researchers, academics, and market representatives.

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

This volume constitutes the proceedings of the 16th International Conference on Intelligent Tutoring Systems, ITS 2020, held in Athens, Greece, in June 2020. The 23 full papers and 31 short papers presented in this volume were carefully reviewed and selected from 85 submissions. They reflect a variety of new techniques, including multimodal affective computing, explainable AI, mixed-compensation multidimensional item response, ensemble deep learning, cohesion network analysis, spiral of silence, conversational agent, semantic web, computer-supported collaborative learning, and social network analysis.