# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 5 Discussion  
The classification that was conducted in this research is based on information about students' personal lifestyles, parent information, and scores in each discipline (lesson). The type of data features used in this study was selected based on studies on student performance evaluation using ML and the data features it had used [15; 24; 39; 40; 42; 52]. These features include all aspects of students' home life, school life, how they use their time, interests, work, etc. Therefore, an evaluation of a student with these features provides a comprehensive result of his/her performance. Overall, 35 attributes were considered for each instance. Next, the dataset was imported into selected models including RF, ANN, LR and SVM, which were trained by a cross-validation technique where k = 10. Among the models used, the ANN model with two hidden layers containing 105 and 70 neurons had the best performance (i.e., 0.83) on the original dataset. Moreover, the LR model was able to provide good performance in some iterations. The highest accuracy performed by this model was on the upper quartile of the chart in Figure 3 that was equal to 0.90. Besides, the performance of the SVM model had the most changes on the decreased training data, and the accuracy of this model decreased. However, the SVM accuracy on the decreased test data was much better; thus, it had the best performance compared to the other models. Moreover, after reducing the dataset from the low-importance features, the RF and LR model still had the same accuracy as in the original dataset. Although the models are able to provide the same performance, they became more efficient and more convenient to use for predicting a new instance. In the case where models are more efficient, non-expert users can use them more conveniently with high precision. Consequently, this will make this system more promising, and the trend of replacing artificial intelligence for educational evaluations will accelerate. Furthermore, models have a few variances that do not improve with hyperparameter tuning in the original dataset. To resolve this problem, more data should be collected and entered into the models to reduce the variance. Our findings are consistent with [44; 51] that have a similar dataset to ours. In terms of the accuracy of ANN, RF and SVM, our models have better accuracy than [39], [34; 39], respectively. In other words, when the models were implemented in the decreased dataset, which only includes the most important and significant features, first, the difference in accuracy in train and test instances was more adjacent, so the variance of models decreased, and the models were more optimized in our data. This demonstrates that some of the unnecessary features in the dataset increase noise and make the algorithms confused. Thus, the algorithms are not able to fit properly on the data. Second, the accuracy of the ANN and SVM models was changed after eliminating features with a lower rank than 1. On the contrary, the ANN model had a noticeable fall among other models, and the SVM model accuracy increased moderately to 0.78 and reached the highest accuracy among other models. The efficiency of the LR and RF remained unchanged, and they were able to offer the same accuracy. To calculate the most important features, the Borate algorithm was employed. This algorithm calculates the importance of each feature and removes the features with low importance. The most important features for evaluating student performance are, in order of importance, absences, second math midterm, attention, second science midterm, first sports midterm, second art midterm, first science midterm, second language midterm, first language midterm, first art midterm, first math midterm, internet use, second sports midterm and study time. In contrast, low-importance features were supervision by the school, internet, higher education, guardian, supervision by family, gender, travel duration, father's and mother's educational background and family relations.