scholarly journals A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

2020 ◽  
Author(s):  
Vasiliki Demertzi ◽  
Konstantinos Demertzis
Keyword(s):  
Recommendation System ◽  
Learning System ◽  
Educational Systems ◽  
Learning Needs ◽  
Pedagogical Strategies ◽  
Substantive Content ◽  
Teaching Content ◽  
Adaptive Educational Systems ◽  
Educational Conditions ◽  
Content Based Filtering

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

scholarly journals A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

2020 ◽  
Author(s):  
Vasiliki Demertzi ◽  
Konstantinos Demertzis
Keyword(s):  
Recommendation System ◽  
Learning System ◽  
Educational Systems ◽  
Learning Needs ◽  
Pedagogical Strategies ◽  
Substantive Content ◽  
Teaching Content ◽  
Adaptive Educational Systems ◽  
Educational Conditions ◽  
Content Based Filtering

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

2022 ◽  
Vol 12 (1) ◽  
pp. 515
Author(s):  
Amina Ouatiq ◽  
Kamal ElGuemmat ◽  
Khalifa Mansouri ◽  
Mohammed Qbadou
Keyword(s):  
Mental State ◽  
Adaptive Systems ◽  
Recommendation System ◽  
Unified Modeling Language ◽  
Social Situation ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Holistic View ◽  
Rule Based

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.

scholarly journals Expanding Learning Presence to Account for the Direction of Regulative Intent: Self-, Co- and Shared Regulation in Online Learning

2015 ◽  
Vol 19 (3) ◽  
Author(s):  
Suzanne Hayes ◽  
Sedef Uzuner-Smith ◽  
Peter Shea
Keyword(s):  
Online Learning ◽  
Online Courses ◽  
Self Regulation ◽  
Theoretical Models ◽  
Underlying Structure ◽  
Learning Needs ◽  
Online Learners ◽  
Online Instructors ◽  
Pedagogical Strategies ◽  
Learning Presence

As the pivotal role of self-regulation has been widely accepted in online learning literature, much interest is focused on identifying pedagogical strategies to help foster regulatory behaviors in online learners. The authors of this article argue that the learning presence (LP) construct, a recently proposed addition to the Community of Inquiry (CoI) theoretical framework of online learning, needs to be included in these conversations. To this end, they re-articulate and clarify the underlying structure of LP by drawing on the theoretical models of self-regulation, co-regulation, and socially shared regulation. They further present examples to illustrate how LP can manifest itself in learners’ discourse in the online learning environment. Finally, they conclude by outlining strategies online instructors can use to help learners execute regulatory behaviors and thus demonstrate LP in online courses.

A Model for an Adaptive Hypermedia Learning System Based on Data Mining Technique

2017 ◽  
pp. 65-85
Author(s):  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Learning Performance ◽  
Adaptive Hypermedia ◽  
Data Mining Technique ◽  
Pedagogical Strategies ◽  
Hypermedia Learning

Since the learning style of each learner is different. Adaptive hypermedia learning system (AHLS) must fit different learner's needs. A number of AHLS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: less attention was paid to the relationship between learning styles and learning performance. This paper proposes an AHLS model based on learning styles and learning performance. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her learning style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

Towards Increasing the Efficiency of E-Learning Systems Using Recommendation System Approach

2020 ◽  
pp. 35-55
Author(s):  
Zameer Gulzar ◽  
L. Arun Raj ◽  
A. Anny Leema
Keyword(s):  
System Approach ◽  
Recommendation System ◽  
Learning Systems ◽  
Learning Objectives ◽  
Learning System ◽  
E Learning ◽  
Flexible Delivery ◽  
Algorithmic Techniques ◽  
Information Optimization ◽  
Almost All

Data mining approaches have been tried in e-learning systems for information optimization and knowledge extraction to make decisions. In recent years, the recommendation system has gained popularity in every field be it e-commerce, entertainment, sports, healthcare, news, etc. However, in e-learning system, the recommender systems were not effectively utilized in comparison to other domains and thus emerged as a bottleneck for almost all e-learning systems for not offering flexible delivery of the learning resources. Current e-learning systems lack personalization features, and the information is presented in a static way despite their varying learning objectives and needs. The aim of recommender system is to personalize the information with respect to learner interest. The objective of this study is to highlight various algorithmic techniques that can be used to improve information retrieval process to provide effective recommendations to learners for improving their performance and satisfaction level.

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