Integrating Knowledge-Based Reasoning Algorithms and Collaborative Filtering into E-Learning Material Recommendation System

2017 ◽  
pp. 419-432 ◽  
Author(s):  
Phung Do ◽  
Kha Nguyen ◽  
Thanh Nguyen Vu ◽  
Tran Nam Dung ◽  
Tuan Dinh Le
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Learning Material ◽  
Knowledge Based ◽  
E Learning

Providing Personalized Services to Users in a Recommender System

2015 ◽  
Vol 10 (2) ◽  
pp. 26-48 ◽  
Author(s):  
Olukunle Oduwobi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Ability ◽  
Learning Material ◽  
Electronic Library ◽  
Learning Materials ◽  
Personalized Services ◽  
E Learning ◽  
Personalized Recommender System ◽  
Content Based Filtering

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

Multi-Tier Knowledge-Based System Accessing Learning Object Repository Using Fuzzy XML

2010 ◽  
pp. 471-492 ◽  
Author(s):  
Priti Srinivas Sajja
Keyword(s):  
Learning Object ◽  
Learning Material ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Cost Benefit Ratio ◽  
Presentation Sequence ◽  
E Learning ◽  
Learning Object Repository ◽  
Common Learning ◽  
Fuzzy Xml

Quality of an e-Learning solution depends on its content, services offered by it and technology used. To increase reusability of common learning material which is accessed by multiple applications, there is a need for user-friendly and cost-effective knowledge-based approach. This chapter discusses basic concepts of learning object repositories; presents work done so far and establishes the need of knowledgebased access of the learning repositories to improve cost-benefit ratio of an e-Learning solution. For this purpose, a multi-tier knowledge-based system accessing a fuzzy XML learning object repository is described with architectural framework and detailed methodology. The working of course tier, reusable LO tier, presentation tier, fuzzy interface tier and application tier is discussed in detail with an example to identify learners’ level and determine presentation sequence accordingly. The chapter concludes by discussing the advantages and questions related to further enhancement.

scholarly journals Event driven Recommendation System for E-commerce using Knowledge based Collaborative Filtering Technique

2020 ◽  
Vol 21 (3) ◽  
pp. 369-378
Author(s):  
Mahesh Kumar Singh ◽  
Om Prakash Rishi
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Evaluation Studies ◽  
Knowledge Graph ◽  
Knowledge Based ◽  
Target User ◽  
Online Trading ◽  
User Data ◽  
Two Phases

The Internet is changing the method of selling and purchasing items. Nowadays online trading replaces offline trading. The items offered by the online system can influence the nature of buying customers. The recommendation system is one of the basic tools to provide such an environment. Several techniques are used to design and implement the recommendation system. Every recommendation system passes from two phases similarity computation among the users or items and correlation between target user and items. Collaborative filtering is a common technique used for designing such a system. The proposed system uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship  among the users and the items. Almost every existing recommendation system is based on one of feature, review, rating, and popularity of the items in which users’ involvement is very less or none. The proposed approach uses about 100 percent of users’ participation in the form of activities during navigation of the web site. Thus, the system expects under the users’ interest that is beneficial for both seller and buyer. The proposed system relates the category of items, not just specific items that may be interested in the users. We see the effectiveness of this approach in comparison with baseline methods in the area of recommendation system using three parameters precision, recall, and NDCG through online and offline evaluation studies with user data, and its performance is better than all other baseline systems in all aspects.

scholarly journals Design and Implementation of Adaptive Recommendation System

2018 ◽  
pp. 101-117
Author(s):  
MagedEla zony ◽  
Ahmed Khalifa ◽  
Sayed Nouh ◽  
Mohamed Hussein
Keyword(s):  
Semantic Web ◽  
Semantic Similarity ◽  
Recommendation System ◽  
Learning Objects ◽  
Just In Time ◽  
Web Based ◽  
Design And Implementation ◽  
Knowledge Based ◽  
E Learning ◽  
Learner Profile

E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study.In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.

scholarly journals Implementation E-Learning Based Moodle on Physics Learning in Senior High School

2019 ◽  
Vol 3 (2) ◽  
pp. 93
Author(s):  
Suritno - Fayanto ◽  
Maria Yosephien Retna Tinon Kawuri ◽  
Adi Jufriansyah ◽  
Danur Dara Setiamukti ◽  
Dwi Sulisworo
Keyword(s):  
High School ◽  
Learning Outcomes ◽  
Learning Process ◽  
Rectilinear Motion ◽  
Learning Material ◽  
Public High School ◽  
Physics Learning ◽  
Knowledge Based ◽  
E Learning ◽  
Learning Interest

<p>The problems faced today by Indonesian teachers are the difficulty of implementing e-learning in the learning process, especially physics learning. During this time the teacher considers using e-learning in the classroom will not be effective. This study aims to explain how the implementation of physics learning by using Moodle-based e-learning in the learning process. In this study, it divided into two aspecs, namely learning outcomes and interest in learning with physics learning material in rectilinear motion. This study uses an experimental class design with a sample size  X class of 10 students of Public High School 1 Piyungan group chosen randomly. This study used the design of one group pretest and posttest using the T-Test while the significance value using N-gain analysis scores and learning interests obtained from the questionnaire. The results of the analysis show an increase in learning outcomes with very high N-gain significance values. Then the percentage of student learning interest reaches 66% which is in the excellent category. So it can be concluded that the application of moodle-based learning can increase learning interest and learning outcomes and 73% of students agree if knowledge based on Moodle applied in the learning process.</p><p> </p><p><strong>Keywords</strong>: e-learning, Moodle, learning management system, learning outcomes, interest in learning</p>

scholarly journals Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System

2021 ◽  
Vol 2007 (1) ◽  
pp. 012032
Author(s):  
Mahesh Kumar Singh ◽  
Om Prakash Rishi ◽  
Akhilesh Kumar Singh ◽  
Pushpendra Singh ◽  
Pushpa Choudhary
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Knowledge Based
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