An adaptive learning frame work for slow learners in an e-learning environment

2018 ◽  
Vol 6 (7) ◽  
pp. 407-412 ◽  
Author(s):  
Lumy Joseph ◽  
Sajimon Abraham
Keyword(s):  
Learning Environment ◽  
Adaptive Learning ◽  
Slow Learners ◽  
E Learning ◽  
Frame Work

scholarly journals Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

2016 ◽  
Vol 6 (2) ◽  
pp. 81-101 ◽  
Author(s):  
Khalid Almohammadi ◽  
Hani Hagras ◽  
Daniyal Alghazzawi ◽  
Ghadah Aldabbagh
Keyword(s):  
Fuzzy Logic ◽  
Learning Environment ◽  
Adaptive Learning ◽  
Learning System ◽  
Superior Performance ◽  
Teaching Models ◽  
Learning Platform ◽  
E Learning ◽  
Interval Type

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

scholarly journals Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment

2020 ◽  
pp. 3-9
Author(s):  
Humam K. Majeed Al-Chalabi ◽  
Aqeel M. Ali. Hussein
Keyword(s):  
Learning Environment ◽  
Adaptive Learning ◽  
Information Seeking ◽  
Working Memory Capacity ◽  
Learning Needs ◽  
Sequential Processing ◽  
Processing Strategies ◽  
Coupling Strategy ◽  
E Learning ◽  
Computer Based

The paper was aimed to identify the ways and to develop the model for the implementation personalised parameter for adaptive learning in the current E-learning environment. The study is qualitative in nature and relied on previous literature. The results highlighted that the personalised parameters which should be considered before the beginning of the course learning process include the level of learner’s knowledge, goals of the leaners, preferences of the language, style of learning, information seeking task, bandwidth, location, and previous level of knowledge. The study also pointed out learning needs, motivation levels, working memory capacity, intelligence, cognitive style, satisfaction, delight, and self-efficacy, the need for help, selfregulated learning, and ongoing interactions, waiting for feedback, ongoing progress, and navigation preference as the parameters for considering in ongoing sessions. The study formulated an implementation design comprising a combination of CBR and RBR strategies. The design is further categorised into standalone and coupling strategies, while the coupling strategy further includes a combination of embedded, co-processing and sequential processing strategies.

scholarly journals Adaptive Virtual Learning Environment based on Learning Styles for Personalizing E-learning System: Design and Implementation

2020 ◽  
Vol 8 (6) ◽  
pp. 3398-3406
Keyword(s):  
Learning Environment ◽  
Learning Styles ◽  
User Interfaces ◽  
Adaptive Learning ◽  
Architectural Design ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Learning System ◽  
Course Content ◽  
E Learning

Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support student’s learning styles. Based from the data mining results of learner behavioral features of five hundred seven (507) tertiary students, an accurate model for classification of student’s learning styles were derived using J48 decision tree algorithm. The model was implemented in a prototype using a framework and a proposed system architectural design of an adaptive virtual learning environment. The study resulted in the development of an adaptive virtual learning environment prototype where learner’s preferences are dynamically diagnosed to intelligently personalize the course content design and user interfaces for them.

Adaptive E-Learning Using Soft Computing Techniques

2020 ◽  
Vol 17 (5) ◽  
pp. 2057-2059
Author(s):  
S. Muralidharan ◽  
Latha Parthiban
Keyword(s):  
Learning Environment ◽  
Soft Computing ◽  
Education System ◽  
Personalized Learning ◽  
Slow Learners ◽  
Learning Path ◽  
Soft Computing Techniques ◽  
E Learning

E-Learning is gaining more importance in the present education system and methodology of learning are moving from instructor-orientation to learner-orientation thereby providing learner with flexible, efficient and personalized learning environment. In this paper, adaptive e-learning using various soft computing techniques needed for achieving adaptation in learning path in e-learning is discussed. Adaptive e-learning becomes necessary for slow learners and challenged people who take their own time for learning due to their impairment.

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