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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.
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 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 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.
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