Recommender System for Integrating Tacit Knowledge in E-learning Environment to Enhance Learning Potential of Learners

2019 ◽  
Author(s):  
Sunil . ◽  
M. N. Doja
2016 ◽  
Vol 22 (4) ◽  
pp. 1455-1477 ◽  
Author(s):  
Soulef Benhamdi ◽  
Abdesselam Babouri ◽  
Raja Chiky

Author(s):  
Abdul Azeez Khan ◽  
Sheik Abdul Khader

<p>E-learning or electronic learning platforms facilitate delivery of the knowledge spectrum to the learning community through information and communication technologies. The transfer of knowledge takes place from experts to learners, and externalization of the knowledge transfer is significant. In the e-learning environment, the learners seek subject expertise to clarify their subject queries, and a learner query can be routed to an expert for externalization of expert knowledge provided the learner knows the subject expert or the expertise group. However, learners new to e-learning systems are not aware of the expertise group to which the query should be sent, which results in time delays, non-response, inaccurate solutions and loss of knowledge capture. Several models have been proposed to resolve this task, but thus far, these efforts have focused completely on returning the most conversant people as experts on a particular topic to retrieve valuable knowledge. To address this problem, we propose an approach that externalizes the tacit knowledge of a subject expert by creating a dynamic query handling system that automatically transfers a user query to the best subject expert.</p>


2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari

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.


Author(s):  
Муса Увайсович Ярычев

В статье рассматривается вопрос о цифровизации школы, как важном условии повышения качества образования. Организованная при помощи электронных форм среда обучения предоставляет ученикам большую самостоятельность. Необходимым условием совершенствования системы образования выступает создание новых, необходимых для цифровой экономики компетенций педагога. The article considers the issue of school digitalization as an important condition for improving the quality of education. The e-learning environment provides students with greater independence. A necessary condition for improving the education system is the creation of new teacher competencies necessary for the digital economy.


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