A New Approach Based on Deep Learning Algorithms to Study Effective Factors of Using Social Networks on Students’ Performance

In the past few years, the emerging technologies of mobile phones have grown rapidly and led to the creation of a new category of social media, which are very efficient mechanisms for collaboration and communication between their users. Social media consists of a variety of web-based tools that enable users to distribute and share new ideas, thoughts, and information in an interactive environment. Social networks and the use of these networks have become an inseparable part of the lives of many students, so it has a direct impact on all aspects of their lives, including academic performance. Therefore, in this research, a new approach based on data mining techniques will be presented to investigate the factors affecting the use of social networks in the academic performance of students, and for this purpose, the deep learning technique and classification will be used. The proposed method has two main phases. In the first phase, the data is prepared for modeling during the three stages of data integration, cleaning, and transformation, and in the final phase, the data is modeled and analyzed using deep learning. The presented method has a favorable output in the feature selection set with a value of 68%, which shows an improvement of 14% compared to the basic method, which can be concluded that about 68% of social networks and the use of the Internet on the learning and efficiency of students is effective. Social networking sites are very useful in education and research considering that they are used in schools for organizational branding, recruiting, and encouraging students and employees to participate. In this research, a method to identify the impact of social networks and internet use on students' learning based on accurate classification has been presented.