Book ChapterDOI
A Review of Adaptive and Intelligent Online Learning Systems
- pp 251-262
TLDR
In this article , the authors present a discussion on the research work done in the area, showing the work done on adaptive learning systems and intelligent tutoring systems, and the combination of adaptive learning system and intelligent learning system can be developed with advanced capabilities and can be more beneficial to the learners than traditional online learning.Abstract:
AbstractEducation is a vital part of everyone’s life. With the advancement of the internet, online learning has gained a wide scope. With the availability of a wide variety of courses and course content available online, learners usually struggle while choosing the course that will be most beneficial to them. It’s mainly because of the learners having different learning styles, different level of understanding, and different knowledge domains. Along with this, the learners also suffer from improper monitoring and evaluation. The adaptive learning system adapts itself as per the learning style of the learner and an intelligent tutoring system helps in the monitoring and evaluation of the learner’s performance. An intelligent tutoring system also provides an immediate and customized response to the learner. With the combination of Adaptive learning system and intelligent learning system, the online learning system can be developed with advanced capabilities and can be more beneficial to the learners than traditional online learning system. There are various learning styles in which learners can be categorized. Various learning style models are Felder Silverman model, VAK model, David Kolb learning style model etc. The paper presents a discussion on the research work done in the area, showing the work done on adaptive learning systems and intelligent tutoring systems.KeywordsAdaptive learning systemIntelligent tutoring systemLearning styleLearning style modelOnline learningPersonalized learning read more
Citations
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Journal ArticleDOI
An improved adaptive learning path recommendation model driven by real-time learning analytics
Nisha S. Raj,V.G. Renumol +1 more
TL;DR: In this paper , a learning path recommendation approach focused on knowledge building and learning performance analysis is proposed, where the difficulty level of the learning resources is tuned based on the real-time performance analysis of the students.
References
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Journal ArticleDOI
Learners on Focus: Visualizing Analytics Through an Integrated Model for Learning Analytics in Adaptive Gamified E-Learning
TL;DR: The Personalized Adaptive Gamified E-learning (PAGE) model is introduced to extend MOOCs by providing new satisfactory levels of learning analytics and visualization in the rich e-learning process that supports the learner’s intervention in the resultant learning analytics.
Proceedings ArticleDOI
Adaptive learning system for higher learning
TL;DR: An adaptive learning system for both lecturers and students, designed to optimize learning based on unique learning pace across the classes, which has the ability to analyze the level of understanding among students according to the cognitive level as required in the module.
Proceedings ArticleDOI
Adaptive learning framework
TL;DR: A new adaptive learning framework is proposed that classifies learner's based on individual preferences in terms of understanding and processing information and resolves the limitation of traditional learning management system thus improving learner’s learning process.
Proceedings ArticleDOI
Adaptive online learning design using moodle
TL;DR: The proposed approach combines two evaluations and two testing, in order to improve LMS Moodle's adaptive capability to accommodate various type of learners' style.
Proceedings ArticleDOI
An Adaptive e-Leaming Model Based on Myers-Briggs Type Indicator(MBTI)
TL;DR: An adaptive e-learning model that is able to determine the MBTI learning styles of students is proposed that can be used by the system as to provide learning materials or e- learning activities in each phase of learning.