Machine Learning based Approaches for Pedagogical Data Analysis
Language: English
Published by Taylor & Francis Ltd (Sales) Jun 2026, 2026
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - 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. …
Seller Inventory # 9781032871905
- Title
- Machine Learning based Approaches for Pedagogical Data Analysis
- Author
- Arpan Deyasi
- Publisher
- Taylor & Francis Ltd (Sales) Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1032871903
- ISBN 13
- 9781032871905
- Item weight
- 553 grams
- Dimensions
- 234x156x16 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Anirban Mukherjee has a Bachelors in Civil Engineering (1994) from Jadavpur University, Kolkata and PhD from Indian Institute of Engineering, Science and Technology (IIEST), Shibpur, India. He has been a Professor in the Department of Information Technology at RCC Institute of Information Technology, Kolkata, India for the past 24 years teaching engineering and management and more than 14 years of experience in academic administration including accreditation of academic programs. His research interests include Computer Graphics, Computational Intelligence, Optimization and Digital Pedagogy. He has co-authored 2 UG engineering (CSE) textbooks and 18 books on Distance Learning Programs of BBA/MBA/BCA/MCA courses of different Universities across India. Besides authoring several papers/chapters published in journals, books, and conferences he is the co-editor of 8 edited books published by international publishers. He is a life member of the Computer Society of India and Fellow of Institution of Engineers (India) and Science Association of Bengal (SAB).
Arpan Deyasi is an Associate Professor in the Department of Electronics and Communication Engineering in RCC Institute of Information Technology, Kolkata, India. He has over 18 years of professional experience in academia and industry. He has a B.Sc. in Physics (Hons), B.Tech and M.Tech in Radio Physics and Electronics, all from University of Calcutta and PhD from Maulana Abul Kalam Azad University of Technology, India in Nanoelectronics. His work spans around in the field of semiconductor nanostructure, semiconductor photonics, and education technology. He has published over 200 peer-reviewed research papers and edited 8 books, and 2 are in press. He has completed two funded projects as a P.I., and another 2 are ongoing. He has already served as technical consultants of various industrial projects. He is associated with different conferences in various aspects, and also serving as Guest Editor of a renowned journal. He is senior member of IEEE, Chair of IEEE Electron Device Society (Kolkata Chapter), Fellow of IE(I), IETE, ISVE. Dr. Deyasi has received several professional awards including Pedagogical field for his performance in SWAYAM-NPTEL.
Soumen Mukherjee has a B.Sc. (Physics Honours) from Calcutta University, MCA from Kalyani University, and ME in Information Technology from West Bengal University of Technology, India. He is a silver medallist in the ME in Information Technology at West Bengal University of Technology (now MAKAUT). He has a Postgraduate Diploma in Business Management from the Institute of Management Technology, Center of Distance Learning, Ghaziabad, India and a PhD in Computer Science from West Bengal State University. He is an Associate Professor, IT Department at RCC Institute of Information Technology, Kolkata, India. He has more than 20 years of teaching experience in the field of information technology, computer science, and applications. He has around 60 research papers published in different important journals, conferences and more than 10 book chapters in different books by major publishers. He has contributed to over 20 books and edited 3 book volumes. He has served as Program and Editorial Chair in International Conference ICCSE 2016 proceedings. He is also served as a reviewer in some reputed journals and prestigious conferences. He got two best paper awards at international and national conferences. His research fields are image processing, soft computing, machine learning, and pedagogy. He is a life member of several institutions like ISCA, CSI, ISTE, and FOSET. He is a Fellow of IETE.
Pampa Debnath is an Assistant Professor in the Department of Electronics and Communication Engineering in RCC Institute of Information Technology, Kolkata, INDIA. She has more than 15 years of professional teaching experience in academics. Her research interests cover Microwave devices, Microstrip Patch antennas, Photonic crystal based integrated circuit, and education technology. She has published several peer-reviewed research papers in journals, conferences, and a few edited volumes. She has already served as an Editor and also as a technical chair of an ICCSE 2016, coordinated a few Faculty Development Programmes, Workshops, Laboratory and Industrial visits, seminars and technical events under the banner of The Institution of Engineers (INDIA) Kolkata section. She has served as a reviewer of some reputed journals and conferences. Prof. Debnath is a member of The Institution of Electronics and Telecommunication Engineers (IETE), Indian Society for Technical Education (ISTE), International Association for Engineers (IAENG).
Lidia Ghosh (Gold-Medalist, M.Tech., JU) is an Assistant Professor in the Department of Computer Application at the RCC Institute of Information Technology, India. She was a Postdoctoral Fellow at Liverpool Hope University, UK, and has received multiple prestigious fellowships, including the Rashtriya Uchchatara Shiksha Abhiyan Doctoral Fellowship. She has published over 50 research papers and serves as a reviewer for top IEEE journals. Her research focuses on Cognitive Neuroscience, Deep Learning, Type-2 Fuzzy Sets, and Human Memory Formation.
"About the title" may belong to another edition of this title.
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