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Books > Computing & IT > Applications of computing > Artificial intelligence
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Advances in Social Networking-based Learning - Machine Learning-based User Modelling and Sentiment Analysis (Hardcover, 1st ed. 2020)
Loot Price: R4,130
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Advances in Social Networking-based Learning - Machine Learning-based User Modelling and Sentiment Analysis (Hardcover, 1st ed. 2020)
Series: Intelligent Systems Reference Library, 181
Expected to ship within 12 - 17 working days
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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.
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