Predicting student dropouts using random forest

Devi, K. Indira; Ratnoo, Saroj

doi:10.1080/09720510.2022.2130570

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…5. This is because adding the value of the number of trees increases the value of the gini impurity so that it approaches 1 [56].…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Random Forest on Quartile Classification Journal

Sucahyo,

Rizqini,

Naufal

et al. 2024

AET

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Journals play a pivotal role in disseminating scientific knowledge, housing a multitude of valuable research articles. In this digital age, the evaluation of journals and their quality is essential. The SCImago Journal Rank (SJR) stands as one of the prominent platforms for ranking journals, categorizing them into five index classes: Q1, Q2, Q3, Q4, and NQ. Determining these index classes often relies on classification methodologies. This research, drawing inspiration from the Cross-Industry Standard Process for Data Mining (CRISP-DM), seeks to employ the Random Forest method to classify journals, thus contributing to the refinement of journal ranking processes. Random Forest stands out as a robust choice due to its remarkable ability to mitigate overfitting, a common challenge in machine learning classification tasks. In the context of approximating SJR index classes, Random Forest, when utilizing the Gini index, exhibits promise, albeit with an initial accuracy rate of 62.12%. The Gini index, an impurity measure, enables Random Forest to make informed decisions while classifying journals into their respective SJR index classes. However, it is worth noting that this accuracy rate represents a starting point, and further refinement and feature engineering may enhance the model's performance. This research underscores the significance of machine learning techniques in the domain of journal classification and journal-ranking systems. By harnessing the power of Random Forest, this study aims to facilitate more accurate and efficient categorization of journals, thereby aiding researchers, academics, and institutions in identifying and accessing high-quality scientific literature.

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“…5. This is because adding the value of the number of trees increases the value of the gini impurity so that it approaches 1 [56].…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Random Forest on Quartile Classification Journal

Sucahyo,

Rizqini,

Naufal

et al. 2024

AET

View full text Add to dashboard Cite

show abstract

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

Kaensar,

Wongnin

2023

EURASIA J Math Sci Tech Ed

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Recently, many global universities have faced high student failure and early dropout rates reflecting on the quality of education. To tackle this problem, forecasting student success as early as possible with machine learning is one of the most important approaches used in modern universities. Thus, this study aims to analyze and compare models for the early prediction of student performance with six machine learning based on Thailand’s education curriculum. A large dataset was collected from the admission scores of 5,919 students during 2011-2021 of 10 programs in the Faculty of Science at Ubon Ratchathani University. The methodology was carried out using Jupyter Notebook, Python 3, and Scikit-Learn to build the models for prediction. To obtain a higher result, we needed not only to find high-performance prediction models, but also to tune hyperparameter configurations consisting of 138 possible different patterns to identify the best-tuned model for each classifier. Furthermore, we investigated significantly important predictors affecting student success for 10 programs in our faculty. In the experiments, the process was divided into two parts: First, we evaluated effective models using a confusion matrix with 10-fold cross-validation. The results showed that random forest (RF) had the highest F1-measure of 86.87%. While predictive models using fine-tuned RF of 10 programs claimed accuracy of about 72% to 93%. Second, we computed the importance of each feature with fine-tuned RF classifiers. The result showed that national test scores (e.g., ONET-English, ONET-Math, ONET-Science, ONET-Social studies, ONET-Thai, and PAT2), entry type, and school grade (e.g., art, English, GPA, health, math, science, and technology) are highly influential features for predicting student success. In summary, these results yield many benefits for other relevant educational institutions to enhance student performance, plan class strategies and undertake decision-making processes.

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Predicting student dropouts using random forest

Cited by 2 publications

References 14 publications

Performance Analysis of Random Forest on Quartile Classification Journal

Performance Analysis of Random Forest on Quartile Classification Journal

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

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