A Recommender System for Learning Objects Personalized Retrieval

2012 ◽  
pp. 182-210 ◽  
Author(s):  
Ana Casali ◽  
Valeria Gerling ◽  
Claudia Deco ◽  
Cristina Bender
Keyword(s):  
Recommender System ◽  
User Profile ◽  
Learning Objects ◽  
Educational Resources ◽  
Agent Architecture ◽  
Profile Data ◽  
Personalized Search ◽  
Content Based Retrieval ◽  
Multi Agent ◽  
The Subject

This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.

Multi-Agent Based Dynamic E-Learning Environment

2011 ◽  
pp. 142-158 ◽  
Author(s):  
Saleh AlZahrani ◽  
Aladdin Ayesh ◽  
Hussein Zedan
Keyword(s):  
Learning Environment ◽  
Learning Objects ◽  
Agent Based ◽  
Development Framework ◽  
The World ◽  
E Learning ◽  
Multi Agent ◽  
The Subject ◽  
And Training ◽  
Agent Development

Grids are increasingly being used in applications, one of which is e-learning. As most of business and academic institutions (universities) and training centres around the world have adopted this technology in order to create, deliver and manage their learning materials through the Web, the subject has become the focus of investigate. Still, collaboration between these institutions and centres is limited. Existing technologies such as grid, Web services and agents are promising better results. In this article the authors support building our architecture Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE) by combining those technologies via Java Agent DEvelopment Framework (JADE). By describing these agents in details, they prove that agents can be implemented to work well to extend the autonomy and interoperability for learning objects as data grid.

scholarly journals Models of Digital Educational Resources Indexing and Dynamic User Profile Evolution

2016 ◽  
Vol 11 (01) ◽  
pp. 26 ◽  
Author(s):  
Hamid Slimani ◽  
N. El Faddouli ◽  
S. Bennani ◽  
N. Amrous
Keyword(s):  
Bayesian Network ◽  
Information Search ◽  
User Profile ◽  
Educational Resources ◽  
Search Process ◽  
Search Activity ◽  
Personalized Search ◽  
Digital Resources ◽  
Dynamic Development

The modelling of the user profile and its integration into the search process is an effective way in personalized information search within a repository of educational digital resources. Therefore, it raises gradually the issue concerning the dynamic development of this profile so as the information requester sets up queries. In our approach presented in this paper, we propose two models for personalized search on digital educational resources. The first is to establish an index of repository resources while the second is to build the user profile and boost its evolution after each query submitted by the user based on a classical Bayesian network representing a search activity.

scholarly journals Contextual Information Retrieval within Recommender System: Case Study "E-learning System"

TEM Journal ◽  
2020 ◽  
pp. 1150-1162
Author(s):  
Mourad Brik ◽  
Mohamed Touahria
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Research Work ◽  
User Profile ◽  
Learning Objects ◽  
Learning System ◽  
Context Information ◽  
Context Modeling ◽  
Semantic Web Technologies ◽  
Ubiquitous Environment

This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition…) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.

scholarly journals A Multi-Agent Architecture and Protocol for Knowledge Production: a Case-study for Participative Development of Learning Objects

10.28945/2467 ◽  
2002 ◽  
Author(s):  
Juan M. Dodero ◽  
Ignacio Aedo ◽  
Paloma Díaz-Pérez
Keyword(s):  
Knowledge Production ◽  
Learning Objects ◽  
Agent Architecture ◽  
Interaction Protocol ◽  
Knowledge Domains ◽  
Management Processes ◽  
Multi Agent

In a distributed eLearning environment, the development of learning objects is a participative task. We consider learning objects as knowledge pieces, which are subject to the management processes of acquisition, delivery, creation and production. A multiple-tier architecture for participative knowledge production tasks is introduced, where knowledge-producing agents are arranged into knowledge domains or marts, and a distributed interaction protocol is used to consolidate knowledge that is produced in a mart. Knowledge consolidated in a given mart can be in turn negotiated in higher-level foreign marts. The proposed architecture and protocol are applied to coordinate the development of learning objects by a distributed group of authors.

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 User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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