Author(s):  
Seung-Min Han ◽  
Mohammad Mehedi Hassan ◽  
Chang-Woo Yoon ◽  
Hyun-Woo Lee ◽  
Eui-Nam Huh

2014 ◽  
Vol 551 ◽  
pp. 670-674 ◽  
Author(s):  
Gai Zhen Yang

When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.


Author(s):  
Mounia Rahhali ◽  
Lahcen Oughdir ◽  
Youssef Jedidi

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.


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