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230620304
Advances in Social Networking-based Learning: Machine Learning-based User Modelling and Sentiment Analysis (Intelligent Systems Reference Library,
Item #:
230620304
NZD 292
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This book presents a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content based on the needs and preferences of individual learners.
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Product Details
- 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 learningenvironment 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.
| Publisher | Springer |
| Publication date | January 21, 2021 |
| Edition | 2020th |
| Language | English |
| Print length | 188 pages |
| ISBN-10 | 3030391329 |
| ISBN-13 | 978-3030391324 |
| Item Weight | 1 pounds (450 grams) |
| Dimensions | 6.1 x 0.43 x 9.25 inches (15.5 x 1.1 x 23.5 cm) |
| Part of series | Intelligent Systems Reference Library |
Product Description
Advances in Social Networking-based Learning: Machine Learning-based User Modelling and Sentiment Analysis (Intelligent Systems Reference Library, 181)
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Features & Benefits
- Explores cutting-edge social networking technologies for education.
- Integrates machine learning for personalized user modeling.
- Focuses on sentiment analysis to enhance the learning experience.
- Offers a novel framework for adaptive e-learning environments.
- Supports collaborative learning and real-time communication.
- Ideal for researchers and developers in personalized educational software.
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