Electronic Resource

Modern Approach to Educational Data Mining and Its Applications

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908000995	EB0001591	620.004 52 Swe m	Central Library (OPAC)	Available

Detail Information

Series Title

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Call Number

620.004 52 Swe m

Publisher

Singapore : Springer Singapore., 2021

Collation

XXVII, 93p.:Ill

Language

English

ISBN/ISSN

978-981-33-4681-9

Classification

620.004 52

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Computational Intelligence

Specific Detail Info

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Statement of Responsibility

BRF

Other version/related

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