e-Learning recommender system for a group of learners based on the unified learner profile approach

2013 ◽  
Vol 32 (2) ◽  
pp. 264-276 ◽  
Author(s):  
Pragya Dwivedi ◽  
Kamal K. Bharadwaj
Keyword(s):  
Recommender System ◽  
E Learning ◽  
Learner Profile ◽  
Profile Approach

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.

e-Learning Recommender System based on Linear Regression

2011 ◽  
Author(s):  
Dumitru Dan Burdescu ◽  
Marian Cristian Mihăescu ◽  
Costel Marian Ionascu ◽  
Bogdan Logofatu
Keyword(s):  
Linear Regression ◽  
Recommender System ◽  
E Learning

Metis

2012 ◽  
pp. 24-42 ◽  
Author(s):  
Jody S. Underwood
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Content Area ◽  
Learning Activities ◽  
Digital Learning ◽  
Design Phase ◽  
Learning Contexts ◽  
Good Advice ◽  
E Learning ◽  
Ready To Learn

Recommender systems in e-learning contexts typically try to “intelligently” recommend actions to a learner based on the actions of previous learners. One of the limitations of such systems is that a lot of data is needed in order to recommend meaningful activities. This chapter describes one approach for addressing this limitation in a framework that uses a structured map of mathematics concepts and processes to power a recommender system that will recommend to students digital learning activities for which they are ready. This recommender system is called Metis, for the Greek goddess of good advice, and is currently in the design phase. Metis takes seriously the idea that to build on the knowledge, skills, and abilities (KSAs) that a student has, it is essential to identify those KSAs. Trying to build on KSAs that a student does not have is misguided. Metis recommends activities linked to KSAs that students are ready to learn, and more standard recommender algorithms further refine the list of recommended activities. Taking this approach has the potential to make activities more engaging, which can lead learners to greater interest in the content area.

scholarly journals Toward a Personalized Learning Path through a Services-Oriented Approach

2019 ◽  
Vol 14 (15) ◽  
pp. 52
Author(s):  
Mohamed Bendahmane ◽  
Brahim El Falaki ◽  
Mohammed Benattou
Keyword(s):  
Information Technologies ◽  
Service Oriented Architecture ◽  
Learning Systems ◽  
Individualized Learning ◽  
Learning Path ◽  
New Information ◽  
Service Oriented ◽  
E Learning ◽  
Learner Profile ◽  
Competency Based

In most existing E-learning systems, activities' content and order are presented in a static manner without taking into consideration the learners characteristics, profiles or competencies. The challenge is to adapt and regulate learning processes according to the learner profile by applying learning models that use new information technologies. There are several adaptation approaches of E-learning environments, such as, adaptive hypermedia system, semantic web, etc. In our proposed system, we adopted a Competency Based Approach to offer each learner an individualized learning path for the acquisition of the competence targeted on the basis of the collaborative filtering. Concerning the technological aspect, the system is implemented as a web services while adhering to a service-oriented architecture. This allows interoperability with heterogeneous learning systems

Recommender System for E-Learning

2014 ◽  
Vol 11 (1) ◽  
pp. 1-4
Author(s):  
M.Thanga raj ◽  
◽  
S. Usha Devi
Keyword(s):  
Recommender System ◽  
E Learning
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