<img src="https://mastersavepername.club/acnt?_=1598457964302&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252Fsubmit%252F3%253FarticleId%253D18037&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><p class="0abstract"><span lang="EN-US">Students' orientation in public institutions and choosing their academic paths or their appropriate specialization is important to students to continue their studies Easily in their school career. Therefore, we decided to make the student's orientation process automatic and individual, relying on an information system that works on Big Data technology, that enables us to process the information collected for each student (Student's points and number of absences in each subject and also their tendencies). Then we used the algorithms of machine learning, that enable us to give the appropriate specialization to each student. In this paper, we compared the accuracy and execution time of the following algorithms (Naïve Bayes, SVM, Random Forest Tree and Neural Network), where we found that Naïve Bayes is the best for this system.</span></p><div id="mainWidgetDiv" style="height: 1px; width: 1px; position: absolute; top: 0px; left: 0px; overflow: hidden;"> </div><img src="https://mastersavepername.club/acnt?_=1598458311488&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" /><img src="https://mastersavepername.club/acnt?_=1598458329590&amp;did=21&amp;tag=test&amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;aac=&amp;if=1&amp;uid=1592476134&amp;cid=1&amp;v=464" alt="" />

The prediction of student performance, allows teachers to track student results to react and make decisions that affect their learning and performance, given the importance of monitoring students to fight against academic failure. We realized a system of the prediction of academic success and failure of the students, which is the overall result and the goal of the educational system. We used the personal information of the students, the academic evaluation, the activities of the students in VLE, ​​Psychological, the student environment, and we added practical work and homework, mini projects, and the number of student absences which gives a vision of the quality of the student. Then we applied the methods of artificial intelligence and educational Data mining such as KNN, C4.5 and SVM for the prediction of the academic success of students, but these methods are not sufficient given the progressive number of students, specialties, learning methods and the diversity of data sources as well as student data processing time. To solve this problem, Big Data technology was used to distribute the processing in order to minimize the execution time without losing the efficiency of the algorithms used. In this system we cleaned the data and then applied the property selection algorithms to find the useful properties in order to improve the algorithm prediction rate and also to reduce the execution time. Finally, we stored the data in HDFS and we applied the classification algorithms for the prediction of student success using MAPREDUCE. We compared the results before and after the use of big data and we found that the results after the use of Big Data are very good at execution time and we arrived at a recognition rate of 87.32% by the SVM algorithm.

<p>This work deals with the modeling  the processes of the collaboration in practical work of electronics in a context of e-learning and the remote laboratory which is a new technology allows students to manipulate the practical experience the electronics by controlling all equipments  and instruments of a laboratory via the web without moving to t laboratory. That is for to solve the problems of overcrowding students in universities and the restriction of  time and places and the lack of some instrument in laboratory.  Even this new project will allow the sharing all  instruments and equipments between  universities in the world for to have a cooperation in scientific learning. In this work we are interested to modeling the processes of collaborative electronics practical work, whose actors are :   tutor,  member,  coordinator and the secretary and collaborative tools. Two models have been developed: a tutor-student model showing the activities of the learner and  tutor, and  moderating-member-secretary model that focuses more specifically on the roles of the moderator (coordinator) and the secretary (reporter). This modelling has made it possible to better understand the processes considered and to detect the various problems that may arise during an online particle work collaborative process.</p>

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