## 3 Materials and Methods
### System Implementation Design  
Our team designed and developed a new educational application with the main goal of reducing ESL and school and university dropouts (Figure 1). It is a Windows Desktop application and can be described as Extract-Transform-Load (ETL). It receives data from other educational applications, such as from MySchool, Moodle, Leak, and Classer. In more detail, MySchool is an online application designed by the Greek Ministry of Education, and it is used by all Greek secondary schools, whereas Moodle is a widely used web application used for asynchronous education. Leakage is a windows desktop application designed and developed by our team that offers telematics services to reduce ESL and educational dropout. Finally, Classer is a web application of the company Vertitech that aims to manage the educational process in the life cycle of an organization such as primary, high school tuition, and general or vocational high school. Our application was programmed in the C# programming language in Microsoft Visual Studio 2017. It utilizes Microsoft Virtual Machine.NET Framework 3.5 libraries so that our application can work in all versions of Microsoft Windows. The application sends automatically training data to the warehouse database from Excel, Access, SQLSERVER. It also sends information via SMS to parents such as information about their children's attendance. An essential requirement of the application is that the data it sends to the cloud warehouse database have to be anonymous, not only because it must comply with the community privacy requirement but also because personal data do not help us make educational decisions. Our application transfers data to the cloud warehouse database, which was developed in SQL SERVER 2017. The warehouse database is designed with a table of events and dimensions tables in a snowflake schema. The table of facts has in each line the attendance of each student per day and the dimension tables most of the characteristics that affect the attendance of the student such as environment, schoolteacher, family, etc. From the cloud warehouse database, we isolated the data that we needed to make educational decisions. The technique we used was that of business intelligence used for many years by companies with many data to determine the behavior of their customers and their organization. Thus, with CUBE and roll-up techniques, we succeeded in isolating an educational phenomenon and managing it per period, per area, per age of students, etc. With the data we isolated with CUBE techniques and with the capabilities of the Tools for Visual Studio (RTVS) tool, we could more easily visualize an educational phenomenon and look for the prediction. The snowflake model, as an extension of the star model, with each dimension table extending outward, supported the role or explained details to multiple tables for external connection. The main point of these tables was for a detailed description of the fact table  
Figure 1: Screenshot of the Extract-Transform-Load (ETL) application. for some dimensions, which can reduce the fact table properties, to enhance the efficiency of the query [42]. As shown in Figure 2, the design of the cloud warehouse database was in the form of a snowflake schema. The transaction fact table included the students' absences per hour in the daily school schedule, the total students' absences per school day, and the students' absences due to the fact of illness. The dimensional tables included project characteristics and characteristics that affected students' attendance such as weather, teachers, grades, and student characteristics (without including properties that reveal the student's identity). In Figure 3, there is a snapshot of the cloud warehouse database. In the \"Absences Fact Table\", information regarding the presence of each student per school hour is shown. Namely, it shows whether the student is present or absent at a specific hour.