학술논문

Classy AA-NECTAR: Personalized Ubiquitous E-Learning Recommender System with Ontology and Data Science Techniques
Document Type
Conference
Source
2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Digital Futures and Transformative Technologies (ICoDT2), 2021 International Conference on. :1-8 May, 2021
Subject
Computing and Processing
Electronic learning
Education
Psychology
Ontologies
Licenses
Real-time systems
Data models
personalised e-learning
ontology
semantic web
educational data mining
learning style
adaptive learning
educational psychology
distance learning
cloud computing
ubiquitous learning
differentiated learning
individualized learning
recommended system
Language
Abstract
Learners have different learning styles each tailored to their own personality. Incompatibility of learning and teaching style is inconvenient. This paper integrates learner behavior modeling, academic web crawling and content retrieval using state of the art technology. This research work aims to propose a personalized ubiquitous learning model to identify learner learning styles and deploy type of content that is corresponding to the learner’s learning style. Felder-Solomon model is one of the models being used for the learner profiling. This gives ease not only to the learners but the pedagogical instructors as well for not making different type of content. Real time monitoring makes the self-adaptive system learn through the learner’s gestures and self-adjusts autonomously. Learners’ aptitude increases, saving time and inconvenience. This will give an easy access to certifying organizations to get more capable skill oriented people.