Evaluation techniques of machine learning in task of reprovation prediction of technical high school students

Melo, Gilberto de; Oliveira, Sanderson M.; Ferreira, Cintia C.; Filho, Ênio Vasconcelos; Calixto, Wesley P.; Furriel, Geovanne P.

doi:10.1109/chilecon.2017.8229739

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Over the past few years, many studies have indicated the effectiveness of ML models for evaluating the performance of students (37,38), and the performance has been evaluated in a variety of tasks such as grading (39), dropout prediction (40), engagement (41), reading ability (42,43) and cognitive diagnosis (44). For instance, in ref (45) bimester; number of faults in the 3rd bimester; number of faults in the 4th bimester; status of the student at the end of the course. The result showed that supervised ML models are useful for evaluating students' performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of a Machine Learning Approach to Model and Assess Student Spatial Intelligence for Advancing STEM Education

Zafari,

Koochi,

Sadeghi-Niaraki

et al. 2023

Preprint

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Spatial intelligence (SI) is the leverage that helps students to have a deeper understanding of concepts of science, technology, engineering and mathematics (STEM) and obtain outstanding academic achievement in STEM. The main objective of this research is to find effective factors in SI. Then, based on those factors, a machine learning (ML) model is developed for estimating students’ SI. To do that, data of 40 features such as demographic, behavioral, environmental and interest were collected from 396 high school students. Chi2, Boruta and Genetic algorithms were employed for identifying the most important features. Subsequently, 18 features were used to develop ML models. The ML models (Random Forest, Support Vector Machines, Multi-Layer Perceptron) achieved accuracy rates of 0.89, 0.84, and 0.78 on the test set. The stack model further increased accuracy to 0.92. The findings of this research have important implications in STEM. The model can estimate students' academic achievement in STEM by measuring their SI and using this for major selection, reducing the dropout rate, predicting student performance, and identifying and helping weaker students. Furthermore, by improving students' SI using the identified features, the quality of education can be enhanced, leading to more efficient and effective learning outcomes for students.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of a Machine Learning Approach to Model and Assess Student Spatial Intelligence for Advancing STEM Education

Zafari,

Koochi,

Sadeghi-Niaraki

et al. 2023

Preprint

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“…Overall, 2,235,804 rows of student information were used in the analysis. For monitoring student performance at a Brazilian technical high school, authors in ref [35] developed an ML system based on NB, SVM, tree-based method (Simple CART), and rule-based method (OneR) algorithms. Their dataset contained information about course name, age, sex, birthplace, course duration, the identification of each discipline studied, number of faults in the first to fourth bimester, and student status at the end of the course.…”

Section: Evaluation Of Student Performancementioning

confidence: 99%

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

et al. 2021

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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.

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“…Additionally, it should be pointed that, data from the fourth bimester is not used, because, in this period, the final student performance (approved or disapproved) is obtained. Further details of each attribute can be found in paper (de Melo et al, 2017).…”

Section: Problem Description: Student Performance Predictionmentioning

confidence: 99%

“…ML has also been used as a tool for decision making, prediction and optimization in the area of education.ML algorithms are proposed to predict the educational performance using the database of an education institution by de Melo et al (2017). The proposed algorithms allow the education professional, even in the first months of the school year, to verify the student's tendency to fail.…”

Section: Introductionmentioning

confidence: 99%

Missing data analysis using machine learning methods to predict the performance of technical students

Melo

Alcalá²,

Furriel

et al. 2020

RBCA

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O aprendizado de máquina (ML) tornou-se uma tecnologia emergente capaz de resolver problemas em muitas áreas, incluindo educação, medicina, robótica e aeroespacial. O ML é um campo específico de inteligência artificial que projeta modelos computacionais capazes de aprender com os dados. No entanto, para desenvolver um modelo de ML, é necessário garantir a qualidade dos dados, pois os dados do mundo real são incompletos, ruídosos e inconsistentes. Este artigo avalia métodos avançados de tratamento de dados ausentes usando algoritmos ML para classificar o desempenho de estudantes do ensino médio do Instituto Federal de Goiânia como no Brasil. O objetivo é fornecer uma ferramenta computacional eficiente para auxiliar o desempenho educacional que permite aos educadores verificar a tendência do aluno a reprovar. Os resultados indicam que o método de ignorar e descartar supera outros métodos de tratamento de dados ausentes. Além disso, os testes revelam que a Otimização Mínima Sequencial, Redes Neurais e Bagging superam os outros algoritmos de ML, como Naive Bayes e Árvore de Decisão, em termos de precisão de classificação.

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Evaluation techniques of machine learning in task of reprovation prediction of technical high school students

Cited by 9 publications

References 5 publications

Implementation of a Machine Learning Approach to Model and Assess Student Spatial Intelligence for Advancing STEM Education

Implementation of a Machine Learning Approach to Model and Assess Student Spatial Intelligence for Advancing STEM Education

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

Missing data analysis using machine learning methods to predict the performance of technical students

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