# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
###### Abstract  
The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education. Student performance; machine learning (ML); educational data mining (EDM); education; artificial intelligence (AI) +
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doing such work [6]. MLs are currently highly advanced and can do more than scoring exams with the answer key. They can provide information about student performance and even perform more conceptual assessments such as scoring the essays [7; 8; 9] or students' engagements [10]. MLs can collect information about how students acted and can evaluate this performance [1]. Machine learning and data mining are powerful tools for instructors and institutions to explore the educational database; this has increased with the assistance of ML, and enabled decision-makers to extract information from data for decisions and policies. The application that ML uses in the educational database is called Educational Data Mining (EDM) [11; 12]. The educational database is available at different levels of detail for different tasks and often expands in several software systems over some time [13]. EDM tries to use features and patterns in a database to make an effective analysis for predicting student performance [14]. This produced information can be used by data scientists, instructors, administrators, a student's parents, etc. [15]. The evaluation of student performance refers to how much a student approaches the educational goals and specifies how well a student learned, how motivated to learn a student is or how good the teaching method was [16]. The information obtained from the evaluation gives teachers this insight so they can make the right decisions to improve the learning of the student and give appropriate feedback. The individual differences of each student, such as personality, motivation, self-efficacy, intelligence and self-control, have a close relationship to his/her performance, so in this research, all these differences were covered by choosing the proper features. The educational system in Iran is divided into two main levels, K-12 education and higher education, consisting of 6 years of primary education and 6 years of high school education. Students spend 24 h in class each week. The curriculum contains mathematics, science, foreign languages and so on. High school students aged 12 to 18 years old are divided into four fields (streams): humanism, science, and technical and vocational streams. Choosing their stream is based on his/her grades and the results of his/her examination, not based on their interest. Furthermore, grades are determined on a scale between 0 and 20 in all levels of education and students are assessed during the semester and end of the semester as well.