Online instrument systems in reality for remote wiring and measurement of electronic in e-learning from LabVIEW+NI ELVIS II vs embedded system+web services

Recently The prestigious universities of the world strive and aim to computerize their distance education service and more specifically the remote practical work, which allows students to manipulate electronic experiments via the web, for to solve a set of problems: pedagogical, management, security, restriction by time and place and the problems the overcrowding of students in universities. This paper will describe the E@SLab system developed by the university Cadi Ayyad member of the e-live project funded by EU in the frame of ERASMUS+E@SLab is based on the latest technologies of development and respects educational and pedagogical standards. E@SLab offers 2 versions different of remote laboratory: first version (s1) is an embedded system its part software is node js+Ubuntu and the part hardware a card pcduino or raspebery. The second version (s2) is based on LABview and its hardware part is the NI ELVIS II pedagogical map. In this paper, we will compare and discuss the architecture, performance of the 2 versions of E@SLAB and review other famous approaches NetLab, VISIR, for comparing with E@SLab with the intention show its singularity

Students' Orientation Using Machine Learning and Big Data

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.

Student Orientation Recommender System using TOPSIS and AHP

 The process of traditional school guidance is carried out by specialists. They make a study of the student files based on the marks of the first year and the second year of the baccalaureate. Considering the progressive number of students and also the lack of time to make the decision, as the selection of the specialty has a great effect on the academic course of the students, we have realized a system of specialty recommendation, to computerize the orientation process and save time. But the major problem is that the students do not care about this process and pay no attention to it despite its importance. As well as the software which makes the orientation is chargeable. To order these specialties to take the best specialty. We have arrived at a problem of multi-criteria which makes it impossible to make a decision with these criteria. Because these criteria do not have the same importance and also are notcompatible, as there are criteria that must be maximum and other criteria must be minimal. To solve this problem, two systems of orientation and academic reorientation of students have been implemented. In both systems, the SMOTE method has been used to balance the learning data in the preprocessing phase. Then in the treatment phase, we sorted the specialties using in the first system, a hybridization of TOPSIS method and the information gain to find the weights of the criteria used, and in the second system, we used a hybridization of the AHP method and information gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method, the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is also higher than the total accuracy of AHP (90.83%). For the complexity of the two methods, it is related to the number of criteria and the number of alternatives. If thenumber of criteria is more than 10 criteria, the complexity of TOPSIS is less than the complexity of AHP, and vice versa. The complexity of the two methods also depends on the number of alternatives, if the number of alternatives exceeds 10, the TOPSIS method becomes more complex than the AHP method. In general, the system based on TOPSIS method and the information gain is more precise than the system based on the AHP method and the information gain. But the complexity of the AHP method is less than the complexity of the TOPSIS method

Predicting Student Success Using Big Data and Machine Learning Algorithms

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

Student Orientation Recommender System using TOPSIS and AHP

The process of traditional school guidance is carried out by specialists. They make a study of the student files based on the marks of the first year and the second year of the baccalaureate. Considering the progressive number of students and also the lack of time to make the decision, as the selection of the specialty has a great effect on the academic course of the students, we have realized a system of specialty recommendation, to computerize the orientation process and save time. But the major problem is that the students do not care about this process and pay no attention to it despite its importance. As well as the software which makes the orientation is chargeable. To order these specialties to take the best specialty. We have arrived at a problem of multi-criteria which makes it impossible to make a decision with these criteria. Because these criteria do not have the same importance and also are not compatible, as there are criteria that must be maximum and other criteria must be minimal. To solve this problem, two systems of orientation and academic reorientation of students have been implemented. In both systems, the SMOTE method has been used to balance the learning data in the preprocessing phase. Then in the treatment phase, we sorted the specialties using in the first system, a hybridization of TOPSIS method and the information gain to find the weights of the criteria used, and in the second system, we used a hybridization of the AHP method and information gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method, the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is also higher than the total accuracy of AHP (90.83%). For the complexity of the two methods, it is related to the number of criteria and the number of alternatives. If the number of criteria is more than 10 criteria, the complexity of TOPSIS is less than the complexity of AHP, and vice versa. The complexity of the two methods also depends on the number of alternatives, if the number of alternatives exceeds 10, the TOPSIS method becomes more complex than the AHP method. In general, the system based on TOPSIS method and the information gain is more precise than the system based on the AHP method and the information gain. But the complexity of the AHP method is less than the complexity of the TOPSIS method