Predicting Students Academic Performance using an Improved Random Forest Classifier

Jayaprakash, S.; Krishnan, Sangeetha; Jaiganesh,

doi:10.1109/esci48226.2020.9167547

Cited by 33 publications

(27 citation statements)

References 10 publications

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“…The results showed that the RF algorithm yielded the lowest MAPE of 6.34%, using academic and demographic factors to predict students' academic performance. This result is compatible with results in [4,6,7,30], which RF gave highest accuracy among other algorithms. The accuracy percentages of RF in [4,6,7,30] were 91%, 93%, 90% and 88.3%, respectively.…”

Section: Discussionsupporting

confidence: 91%

“…This result is compatible with results in [4,6,7,30], which RF gave highest accuracy among other algorithms. The accuracy percentages of RF in [4,6,7,30] were 91%, 93%, 90% and 88.3%, respectively.…”

Section: Discussionsupporting

confidence: 91%

“…Predicting students at risk of academic failure at the year's level was considered in [4,6,7]. The authors have examined the impact of economic, psychological, and demographic factors on the students' academic performance.…”

Section: E-learning and Distance Learning Environmentmentioning

confidence: 99%

“…Early prediction of students' academic performance can assist educational institutions to take the appropriate action at the right time, and making a suitable plan to improve the academic performance of low performing students [3]. However, predicting students' academic performance accurately can be challenging as it is influenced by numerous factors (e.g., academic, marital, and economic factors) [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Web-Based Prediction System for Students’ Academic Performance

Alboaneen

Almelihi

Alsubaie

et al. 2022

Data

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Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 6.34%, and the academic factors had a higher impact on students’ academic performance than the demographic factors, the midterm exam score in the top. The developed web-based prediction system is available on an online server and can be used by tutors.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 91%

Section: E-learning and Distance Learning Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Web-Based Prediction System for Students’ Academic Performance

Alboaneen

Almelihi

Alsubaie

et al. 2022

Data

View full text Add to dashboard Cite

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“…In This paper, we used machine-learning algorithms to predict Students' learning di culties using LMS, while many researches used it to predict the performance of the students. (Jayaprakash et al, 2020), focused on identifying the students at risk, built a model used Random Forest, Naïve Bayes and other ensemble methods, which classi ed the attributes, to use as a temporary or early warning mechanism to improve student performance. They concluded that factors such as gender, family size, parental status, maternal and paternal education, maternal and paternal function, are some of the in uencing factors that can negatively affect student achievement.…”

Section: Related Workmentioning

confidence: 99%

Student Performance Prediction Using Machine Learning Techniques

El‐Hafeez

Ahmed

2022

Preprint

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With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field has garnered significant attention in recent times. In this paper, we used ten machine-learning algorithms (Logistic Regression, Decision Tree, Random Forest, SGD Classifier, Multinomial NB, K-Neighbors Classifier, Ridge Classifier, Nearest Centroid, Complement NB and Bernoulli NB) to build a prediction system based on artificial intelligence techniques to predict the difficulties students face in using the e-learning management system, and support related decision-making. Which, in turn, contributes to supporting the sustainable development of technology at the university. From the results obtained, we found the important factors that affect the use of E-learning to solve students' learning difficulties by using LMS.

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