e-Learning Recommender System for Teachers using Opinion Mining

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.4.158 ◽

2018 ◽

Vol 2 (4) ◽

pp. 271 ◽

Cited By ~ 3

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.

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

Expert Systems ◽

10.1111/exsy.12061 ◽

2013 ◽

Vol 32 (2) ◽

pp. 264-276 ◽

Cited By ~ 29

Author(s):

Pragya Dwivedi ◽

Kamal K. Bharadwaj

Keyword(s):

Recommender System ◽

E Learning ◽

Learner Profile ◽

Profile Approach

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e-Learning Recommender System based on Linear Regression

Computers and Advanced Technology in Education ◽

10.2316/p.2011.734-002 ◽

2011 ◽

Author(s):

Dumitru Dan Burdescu ◽

Marian Cristian Mihăescu ◽

Costel Marian Ionascu ◽

Bogdan Logofatu

Keyword(s):

Linear Regression ◽

Recommender System ◽

E Learning

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Metis

Educational Recommender Systems and Technologies ◽

10.4018/978-1-61350-489-5.ch002 ◽

2012 ◽

pp. 24-42 ◽

Cited By ~ 7

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.

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