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 Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

2019 ◽  
Vol 15 (2) ◽  
pp. 85-107
Author(s):  
Zameer Gulzar ◽  
L. Arun Raj ◽  
A. Anny Leema
Keyword(s):  
Recommender System ◽  
Hybrid Approach ◽  
Learning Systems ◽  
E Learning ◽  
Hybrid Recommender System ◽  
The Individual ◽  
Hybrid Recommender ◽  
Nearest Neighborhood ◽  
Personal Interests ◽  
Hybrid Methodology

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

Sign in / Sign up

Export Citation Format

Share Document