Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

2021 ◽  
Author(s):  
Sami Alshmrany
Keyword(s):  
Learning Environment ◽  
Learning Style ◽  
Adaptive Learning ◽  
Lévy Flight ◽  
Levy Flight ◽  
E Learning

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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.

User Modeling Approach for Dyslexic Students in Virtual Learning Environments

2017 ◽  
Vol 7 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Fatima Ezzahra Benmarrakchi ◽  
Jamal El Kafi ◽  
Ali Elhore
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Learning Style ◽  
Adaptive Learning ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Virtual Learning Environments ◽  
Conventional Instruction ◽  
Cognitive Traits ◽  
The Relationship

Dyslexia or reading disability is one of the most common learning disabilities. It is defined as a disorder manifested by difficulty in learning to read despite conventional instruction, adequate intelligence, and sociocultural opportunity. In this paper the authors focus on the potential benefits of the use of Information and Communication Technology (ICT) for students with dyslexia to promote the learning process, by considering the relationship between their learning style preferences and their cognitive traits in virtual learning environments. At this propose the authors investigated the relationship between dyslexic's learning style and cognitive trait within the hypothesis that dyslexic learners may have possible preferences in virtual learning environment, which may be used to improve the dyslexic user modelling. The aim of this paper is to provide an adaptive learning environment for users with dyslexia based on their learning styles preferences and their cognitive traits.

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.

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