Predicting and analyzing the performance of students through data mining techniques to improve academic performance

Background and Objectives: Nowadays, significant advancements in information technology and communication field in different societies are seen. Given that these advancements, universities as a leading institution in the field of science, have moved towards electronic processes in the management of education and educational environments, there are databases with a large amount of information. By analyzing this massive data of educational systems, methods can be provided to improve the educational status of students. Educational data mining has sought to discover the knowledge contained in the data of the educational system. One of the applications of educational data mining is to predict students' academic performance. Predicting students' academic performance and providing useful solutions is of particular importance in the success of educational systems and can help managers make the right decisions to increase the efficiency of the educational system and better student performance. The purpose of this paper is to identify the effective indicators on academic performance, predict students' academic status using data mining techniques, and finally present a new trend for modifying unit selection and educational strategies to increase the efficiency of the education system. Methods: steps of this research are determined according to CRISP model. In current research, Databases containing 9 datasets of specialized courses in industrial engineering were used. The students' grade was bachelor's degree. Indicators affecting student performance have been identified based on previous researches and expert opinions. Demographic data and academic records of undergraduate students are entered in database. After data preprocessing, 13 attributes are selected, different models were proposed to predict student's academic status in the next semester. Then, a comparison between the results of 4 different algorithms has been done. Findings: All 13 attributes are identified to be effective according to information gain and gain ratio. This 13 attributes as follow: GPA, Total passed units, Number of conditional terms, Type of admission, Marital status, Gender, University admission year, Living place , Age, Current semester, Prerequisite course score, instructor of the course, Repeat the course. Between of 4 considered models, the Logit Boost algorithm is known as the best model in categorizing in two class and multi-class according to the accuracy rate and ROC. Conclusion: Because of acceptable performance of data mining algorithms, the use of these algorithms in predicting student performance is appropriate and the proposed model can be used as a support tool for decision making in educational systems. Finally, according to the obtained results and the opinion of academic experts, the unit selection process was redesigned. The proposed model can be used as a decision support tool in educational systems. Finally, due to the results obtained and the opinions of the academic experts, the process of unit selection was redesigned. The presented process uses the available data in educational systems and data mining science, provides useful knowledge to decision-makers to make the right and appropriate decision. Decision makers can make appropriate decisions by examining the predictions made by the data mining algorithm and obtaining useful information, in order to make the educational system more efficient.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids