Data Mining Approach for Making Prediction of Students Success

Osmanbegovi, Edin; Agi, Haris; Suljic, Mirza

doi:10.2991/icaicte.2013.145

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…This Algorithm is a combination of several tree predictors, or it can be called a decision tree, where each tree depends on a random vector value sampled freely and evenly on all trees in the forest [31]. The prediction results from the Random Forest get the most results from each decision tree [32].…”

Section: ) Support Vector Regression (Svr): Svr Is a Development Of T...mentioning

confidence: 99%

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Apriyadi¹,

Ermatita²,

Rini³

2023

IJACSA

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Improving student learning performance requires proper preparation and strategy so that it has an impact on improving the quality of education. One of the preparatory steps is to make a prediction modeling of student performance. Accurate student performance prediction models are needed to help teachers develop the potential of diverse students. This research aims to create a predictive model of student performance with hyperparameter optimization in the Support Vector Regression Algorithm. The hyperparameter optimization method used is the Metaheuristic Algorithm. The Metaheuristic Algorithms used in this study are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). After obtaining the best SVR hyperparameter, the next step is to model student performance predictions, which in this study produced two models, namely PSVR Modeling and GSVR Modeling. The resulting predictive modeling will also be compared with previous researchers' prediction modeling of student performance using five models: Support Vector Regression, Naïve Bayes, Neural Network, Decision Tree, and Random Forest. The regression performance metric parameter, Root Mean Square Error (RMSE), evaluates modeling results. The test results show that predictive student performance using PSVR Modeling produces the smallest RMSE value of 1.608 compared to predictions of student performance by previous researchers so that the proposed prediction model can be used to predict student performance in the future.

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Section: ) Support Vector Regression (Svr): Svr Is a Development Of T...mentioning

confidence: 99%

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Apriyadi¹,

Ermatita²,

Rini³

2023

IJACSA

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“…In a related study to categorize student performance into distinct classes and stages, Bharti [6] also revealed that Nave Bayes delivers the best Specificity when compared to the C.5 decision tree. According to Osmanbegovi and Sulji [7], when predicting True Negative values, the Nave Bayes algorithm outperforms MLP and C.45.…”

Section: Related Workmentioning

confidence: 99%

“…According to Ashari, Paryudi and Tjoa [18], Naïve Bayes is the best algorithm for classifying students' performance based on Precision, Recall, Fmeasure, Accuracy, and AUC as it could outperform Decision Tree and k-Nearest Neighbor. Also, Osmanbegovi and Suljic [7] reported that the Naïve Bayes algorithm is more efficient when compared with MLP and C.45 in predicting True Negative values.…”

Section: Critical Analysis Of Classification Algorithmsmentioning

confidence: 99%

Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance

Adane

Deku

Asare

2023

JAMCS

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The advancement in technology has contributed largely to the application of data mining in education in recent times. However, selecting appropriate algorithm(s) to “mine” knowledge about educational data presents a difficult challenge to researchers and analyst. This paper contributes to the use of classification algorithms in academic performance prediction. The predictive ability of four popular algorithms; C4.5 Decision tree (CDT), Multilayer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF) algorithms were compared. The models were built using student dataset from selected private senior high schools in Ghana. The comparative analysis of the algorithms was made based on their Accuracy, Recall, Specificity, F-Measure and Running time. On all the training and test ratios; 80:20, 70:30 and 10-fold cross validation, the results indicated that all the algorithms performed well in the classification. However, the Naïve Bayes algorithm performed significantly better than the MLP and CDT on some ratios. The running time of the NB, CDT and RF were the quickest while MLP took the longest time.

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An Exploratory Study of Students’ Learning Performance In Flipped Classroom Using Decision Tree and Regression

Bashir

Ahmed

Marouf

2020

2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

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Data Mining Approach for Making Prediction of Students Success

Cited by 3 publications

References 4 publications

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance

An Exploratory Study of Students’ Learning Performance In Flipped Classroom Using Decision Tree and Regression

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