Modeling and Implementing Ontology for Managing Learners’ Profiles

Abstract

Abstract-This paper presents an issue that is important to consider when developing a learning environment whose field is constantly evolving mainly in terms of the use of training platforms. Research in this field has enabled the successful use of information technologies for the benefit of human learning, while placing the learner at the heart of pedagogic situations. It is also an environment that integrates human agents (tutors, learners) and artificial (computers) and allows them to interact locally or through computer networks, as well as conditions for accessing local or distributed training resources. Moreover, several computing environments for human learning (CEHL) platforms are available on the web for free access. These platforms are environments that offer a learner a multitude of courses in various formats in order to satisfy the learner's desire to learn. Several CEHL platforms are available on the web for free access. But learning itself is not enough and that is why a new generation of advanced learning systems that integrate new pedagogical approaches giving the learner an active role to learn and acquire knowledge has emerged by offering more Interactivity and incorporating a more learner-centered vision. These new generations of advanced learning systems adapt to learners and their profiles by taking into account their cognitive, intellectual and motivational characteristics. An adaptation that cannot be achieved without the complicity of ontological engineering, which plays a very important role in the sharing of knowledge between humans and computers, and between computers and sharing, and reuse of concepts through computational semantics. By the same way, this paper aims at creating a process of modeling and managing profiles of learners based on ontology whatever the learning situation may be. This management process is implemented in computer's environment based on the learner's ontology that supports the learner by detecting the gaps in several factors in order to improve them and adapt the pedagogical content to the learner's profile.

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

“…This section intends to group learners by preferences and degrees according to the Sequential/Global dimension of the FSLSM [34]. After analyzing the learners' features, their traces, and the frequency of their activities in the LMS, several taxonomies and learner profiles were suggested [36], [33]. But only active learners who are involved and explored the most resources in the Learning environment were selected [5].…”

Evaluation and Comparative Analysis of Semantic Web-Based Strategies for Enhancing Educational System Development

“…The proposed approach helps the education system to develop new curriculum and syllabus according to the user requirements. Due to the latest digital technology there is a rapid development in the learning technology also pro from e-learning 1.0 to e-learning 3.0 in in this time (Adil & Lahcen, 2017). E-learning 1.0 model all the study material is created and stored at the cloud services (Manasrah et al, 2019) server for the students.…”

“…E-learning 1.0 model all the study material is created and stored at the cloud services (Manasrah et al, 2019) server for the students. In e-learning 2.0 model students can interact with learning model and provide there feedback to it (Adil & Lahcen, 2017). In e-learning 3.0 model, users get the personalized learning in which thing as per their preference they get the study material.…”

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

“…For other researchers, learners feel isolated from each other when they enrolled in a course [5]. For some scientists, the courses are not adjusted to the learners' orientations and preferences [6]. In that sense, the problem of detecting how learners, study and acquire new skills and knowledge has been raised [3], [4].…”

“…In that regard, we are proposing to use K-means clustering algorithm [4] implemented on learners' behaviors to group them according to their engagement level. Then the learners are classified using a decision tree classifier adjusted by a well-defined set of decision rules [6].…”