Modern Approach to Educational Data Mining and Its Applications

Soni Sweta (Autor)

Buch | Softcover

93 Seiten

2021 | 1st ed. 2021
Springer Verlag, Singapore
978-981-334-680-2 (ISBN)

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This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life’s problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners’ preferences minutely and provide adaptive learning materials to them in a way most appropriately.

Dr. Soni Sweta received her Master of Technology degree from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, in 2011. She successfully completed her Ph.D. degree in the area of Soft Computing and Data Mining in Computer Science & Engineering from Birla Institute of Technology Mesra, Ranchi, in 2018. She has published more than 12 research papers in high-impact peer-reviewed international journals, 2 papers in national journals and 4 book chapters. Her research areas are soft computing, artificial intelligence, machine learning, data science and data mining. From 2004 to 2010, she has worked as Assistant Professor and Visiting Faculty in Computer Science & Engineering departments of different engineering colleges including MLB College; SNGPG College; Scope Engineering College, Bhopal; NSIT Patna; and CIPET Lucknow. She is presently working as Assistant Professor in Amity University Jharkhand, Ranchi, India.

Educational Data Mining in E-Learning System.- Adaptive E-Learning System.- Educational Data Mining Techniques with Modern Approach.- Learning Style with Cognitive Approach.- Framework with Stakholders in Adaptive E-Learning System.- Personalization Based on Learning Preference.- Recommender System to Enhancing Efficacy of E-Learning System.

Erscheinungsdatum	29.01.2021
Reihe/Serie	SpringerBriefs in Applied Sciences and Technology
Reihe/Serie	SpringerBriefs in Computational Intelligence
Zusatzinfo	34 Illustrations, color; 4 Illustrations, black and white; XXVII, 93 p. 38 illus., 34 illus. in color.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Sozialwissenschaften ► Pädagogik
	Technik
Schlagworte	adaptive e-learning • Adaptive Framework • Cognitive Factor • Data Science • educational data mining • Intelligent system • Learning Analytics • Learninggap • Learning Preferences • machine learning • recommendation system
ISBN-10	981-334-680-9 / 9813346809
ISBN-13	978-981-334-680-2 / 9789813346802
Zustand	Neuware