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Machine Learning based Approaches for Pedagogical Data Analysis.
・ISBN 978-1-032-87190-5 hard GB£ 124.99
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| 著者・編者 | Mukherjee, Anirban / Deyasi, Arpan / Mukherjee, Soumen / Debnath, Pampa / Ghosh, Lidia (eds.), |
|---|---|
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2026 |
| ページ数 | 258 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-57853＞ |
解説
The use of intelligent technologies to enhance instruction and learning is introduced in pedagogy-based learning-teaching perspective. It covers digital library resources, AI-based tools, data analysis techniques, and NLP and NLU-powered smart assistants. Students will realize their improved efficacy through use of expandable AI systems improve educational efficiency, automate repetitive chores, and enable personalized learning. The course offers useful skills for implementing contemporary AI methods in educational institutions, classrooms, and online learning settings.
This book provides concise summary of forthcoming Intelligent Tools and Techniques that are using AI-based Learning-Teaching systems to shape contemporary education. It describes how NLP and NLU applications enhance intelligent teaching assistants, showcases sophisticated library resources for promoting informal learning. The book delivers a succinct but thorough approach for implementing scalable, effective, intelligent solutions that improve learning environments across a variety of educational settings through focused insights into educational data analysis and frameworks for expandable AI.
Teachers, researchers, and students who wish to apply intelligent technology in the classroom are the target audience for this book. It works well for developers making intelligent learning tools, librarians overseeing digital resources, and educators investigating AI-based approaches. The book provides clear instructions on using AI, data analysis, and intelligent systems to enhance teaching, learning, and educational resource management, which will be beneficial to academic institutions, policymakers, and EdTech experts.
Key features:
- Contains applications of machine learning in performance analysis of students, which is helpful in designing rubrics for accreditation.
- Deals with comparative study about outcome-based education and conventional educational system through application of statistical techniques.
- Analyses role of emotional intelligence in measuring holistic performance of students
- Evaluates different pedagogical approaches like active, authenticate, flipped, blended learning using neural network approaches.
- Proposes different mathematical models for implementation of OBE for technical Institutions.