IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Survey on the Use of Adaptive Learning Techniques Towards Learning Personalization

A Survey on the Use of Adaptive Learning Techniques Towards Learning Personalization
View Sample PDF
Author(s): Sonali Banerjee (Supreme Knowledge Foundation Group of Institutions, India), Kaustuv Deb (Supreme Knowledge Foundation Group of Institutions, India), Atanu Das (Netaji Subhash Engineering College, India)and Rajib Bag (Supreme Knowledge Foundation Group of Institutions, India)
Copyright: 2021
Pages: 19
Source title: Handbook of Research on Modern Educational Technologies, Applications, and Management
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3476-2.ch049

Purchase

View A Survey on the Use of Adaptive Learning Techniques Towards Learning Personalization on the publisher's website for pricing and purchasing information.

Abstract

E-learning has a great impact on learners today. E-learning supports enhancing learner knowledge anytime, anywhere with lesser efforts than traditional models. In these situations, nonlinear approaches often modify teaching and learning strategies according to students' needs, and hence, automated machine-guided approaches seem useful in the name of adaptive learning. It identifies individual learner styles and provides the most suitable strategy that fits each learner as a case of personalization. Adaptive learning uses personalization for continuously improving student outcomes. Personalized learning takes place when e-learning systems use educational experience supporting desires, objectives, endowments, and curiosities of each individual learner. This work has reviewed the recent developments in the problem area of learning personalization through adaptive learning. Then the solution domain methods are compared to identify the knowledge and technology gap from their limitations. These analyses help to identify research potentials in learning technology for future works.

Related Content

Marlett Jasmin Blas Rivera. © 2024. 24 pages.
Mario Muñoz Mercado. © 2024. 31 pages.
Tahir Iqbal. © 2024. 31 pages.
Nadim Akhtar Khan. © 2024. 20 pages.
Sandra Viridiana Cortés Ruiz. © 2024. 26 pages.
María Elena Zepeda Hurtado, Claudia Angélica Membrillo Gómez, Francisco Javier Arias Candanosa. © 2024. 23 pages.
Renu Prajapati, Sandhya Gupta. © 2024. 29 pages.
Body Bottom