Hybrid Fuzzy Recommendation System for Enhanced E-learning

2017 ◽  
pp. 21-32 ◽  
Author(s):  
Padmaja Appalla ◽  
Rajalakshmi Selvaraj ◽  
Venu Madhav Kuthadi ◽  
Tshilidzi Marwala
Keyword(s):  
Recommendation System ◽  
E Learning

scholarly journals Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

2015 ◽  
Vol 67 (1) ◽  
pp. 99-104 ◽  
Author(s):  
Gabroveanu Mihai
Keyword(s):  
Association Rules ◽  
Management System ◽  
Recommendation System ◽  
Learning Management System ◽  
Distributed Learning ◽  
Learning Management Systems ◽  
Management Systems ◽  
User Data ◽  
E Learning ◽  
Learning Management

Abstract Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

A fog based recommendation system for promoting the performance of E-Learning environments

2020 ◽  
Vol 87 ◽  
pp. 106791
Author(s):  
Taghreed S. Ibrahim ◽  
Ahmed I. Saleh ◽  
Nehad Elgaml ◽  
Mohamed M. Abdelsalam
Keyword(s):  
Learning Environments ◽  
Recommendation System ◽  
E Learning

An Analysis on E-Learning and Its Recommendations

2021 ◽  
pp. 166-187
Author(s):  
Lalitha T. B. ◽  
Sreeja P. S.
Keyword(s):  
Personal Growth ◽  
Recommendation System ◽  
Professional Competence ◽  
Holistic Approach ◽  
Global Village ◽  
Approach To Learning ◽  
E Learning ◽  
Minimal Effort ◽  
The Web

Education provides a predominant source of worldly knowledge around us and changes the perspective of the living society as a global village. However, education has revealed fragmentary remains in the professional competence and personal growth of the learners without the involvement of online learning. E-learning brings out a broader vision of sources to the learners available over the web with the holistic approach to learning from anywhere without cost and minimal effort. The proposed theoretical framework analyses the long-term evolution of e-learning and its effect on mankind. The various methods, technologies, and approaches of e-learning that exist in various forms were discussed exponentially according to the range of necessities among the learners. The recommendation system plays a pivotal role in referring contents and enhancing the learning environment. The education promoted to the learners through the recommendations system over their personal preferences were explored here in detail.

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

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.

Sign in / Sign up

Export Citation Format

Share Document