A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

Mostafa Zafari,Abolghasem Sadeghi-Niaraki,Ali Esmaeily,Soo-Mi Choi

doi:10.3390/app112311534

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.

Highlights

Artificial intelligence (AI) has always been a hot topic for discussion because, in the twenty-first century, the world is run by this technology in almost all spheres of life [1].Machine learning (ML) is a branch of artificial intelligence that systematically applies algorithms to find the combination of basic relationships among data and produces information.machine learning (ML)’s main purpose is to predict future events or future scenarios that are unknown for computers [2,3]
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
Due to the different range of feature values, data were normalized between 1 and 2 because some models such as support vector machines (SVM) are sensitive to these different scales

Summary

Introduction

Artificial intelligence (AI) has always been a hot topic for discussion because, in the twenty-first century, the world is run by this technology in almost all spheres of life [1].Machine learning (ML) is a branch of artificial intelligence that systematically applies algorithms to find the combination of basic relationships among data and produces information.ML’s main purpose is to predict future events or future scenarios that are unknown for computers [2,3]. One of the main advantages of ML is that it can complete complex and time-consuming tasks, and time spent on this work is freed up to use in other matters [4]. This time can be used by teachers to work on progress, interact with students and prepare for classes [5]. 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].

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Methods

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Discussion

Conclusion