# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 3 Materials and Methods
### Dataset  
The dataset used in this study was collected from 459 high school students who have been studying in different levels and different fields such as humanism, science, and technical studies in 2020-2021. One of the high schools is a boys' school and another is a girls' school. Both high schools are located in Arian Shahr, South Khorasan Province, Iran. Various aspects can be considered for evaluating the performance of students, and different conclusions can be obtained from the perspective of each one. Hence, we must have extensive features for an accurate assessment. Through a scrutiny of the literature regarding the integration of AI in education and evaluating student performance with ML, the most important and influential characteristics that expressed student performance in these studies were identified. These studies are mentioned in the related work section. Many features can be considered for student evaluation. In general, students' actions are  
Figure 1: An overview of the research approach. very decisive and determinative because the performance of a student depends on his actions and the manner they were conducted. In this study, students' actions are categorized into two main categories: a behavioral category that represents the usual and individual behavior of the student and grades that show the student's efficiency in different fields of study. Moreover, demographic features of students and their families were included to consider environmental factors affecting the students. These selected features give general and significant information about student efficiency, and students are comprehensively monitored. A total of 65 features were identified as suitable features. Too many questions cause fatigue in students and their focus is decreased, so the quality of the answers is reduced. To optimize data collection, we reduced the number of characteristics to 35 by eliminating less appropriate options which were not too important for the evaluation and were too time-consuming and costly for collecting. We kept only features that had a direct relationship with the performance of the students. The 35 selected features cover all aspects of a student and are a proper measure for performance evaluation. After that, according to the required features, we designed questions that students responded to by selecting existing options. One-to-one surveys were conducted with the students. Three major types of data that were collected from each student included demographic, behavioral, and grades data. Behavioral and demographic data were collected through the online questionnaire and grades were obtained from teachers. Table 1 indicates the attributes of the dataset that elaborate the 35 features used in this research and each value of the feature. In detail, demographic data describe the personal information of each student. In this part of the data, families with more than three members and less than three members are labeled by L and S, respectively. The quality of family relationships is categorized into four levels. The student's guardian, parents' education and parents' jobs were demanded. To determine parents' jobs, five overall categories were determined as follows: (1) education and social occupations such as teachers, the press, psychologists, etc. ; (2) health occupations such as doctors and nurses, etc. ; (3) service occupations such as in sales, sports, tourism, etc. ; (4) agriculture and environmental occupations such as botany, livestock, etc. ; and (5) engineering. The behavioral data of the students include information such as routine personal and educational life. The level of socialization of the student is characterized by friendly relationships and go-out rate. How students use non-school time was determined with questions about the amount of internet, television and video game usage. Other features, such as extra classes, identify informal classes outside of school hours.