Predicting Student Academic Performance using Support Vector Machine and Random Forest

Alamri, Leena H.; Almuslim, Ranim S.; Alotibi, Mona S.; Alkadi, Dana K.; Khan, Irfan Ullah; Aslam, Nida

doi:10.1145/3446590.3446607

Cited by 25 publications

(16 citation statements)

References 12 publications

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“…Although there are many classifiers available, researchers have found Naïve Bayes, k -NN and maximum entropy algorithms working better than other algorithms like ensemble classifiers (Kumar and Goyal, 2018; Jitpakdee and Uyyanonvara, 2017; Prasanna et al. , 2019; Alamri et al. , 2020).…”

Section: Methodsmentioning

confidence: 99%

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Ray

Bala

Rana

et al. 2022

AJIM

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PurposeThe widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.Design/methodology/approachIn this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.FindingsOverall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.Originality/valueThis study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

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Section: Methodsmentioning

confidence: 99%

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Ray

Bala

Rana

et al. 2022

AJIM

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“…The study also examined the contribution of input variables to the prediction of the output variable. [41] Leena H. Alamri et al (2020) in their research on datasets which consisted of records for mathematics and Portuguese language courses, used classi cation algorithms SVM and RF which has shown both SVM and RF algorithms applied to the datasets gave high accuracy in binary classi cation, reaching 93% accurate prediction. [15] Muhammed Berke YILDIZ et al (2020) focused on ninth-grade students' data, utilizing data mining techniques to address a classi cation problem, speci cally predicting academic success.…”

Section: Related Workmentioning

confidence: 99%

“…It is used for classi cation and regression tasks. [15,16,17,18,19,20,21]. The purpose behind using this algorithm is just classi cation.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Performance Evaluation of Different Machine Learning Algorithms on Student Dataset Clustered by K-means Algorithm

Kudale,

Rajpoot

2024

Preprint

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This research paper presents an innovative approach to cluster the dataset and apply the different machine learning algorithms. For clustering, K-Means is used. Before applying K-Means, estimation of value of K is done by using the Elbow method followed by Silhouette method. For the entire experimental work, the Kaggle dataset has been taken into account. K-Means clustering used over here offers five different clusters. Here, each cluster shows identification of distinct student. Subsequently, four prominent machine learning algorithms—K-Nearest Neighbor (KNN), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) are applied and the performance metrics are measured. The comparative analysis of the machine learning algorithms reveals varying levels of accuracy, precision, recall, and F1-score across different clusters. The outcomes highlight the algorithm that exhibits superior performance in this specific context. Here the highest accuracy i.e. 92.00% is achieved with Random Forest algorithm.

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“…In the literature on machine learning models, two main approaches are commonly found: classification and regression. Classification is focused on predicting whether a student approve the semester ( Asif et al, 2017 ; Polyzou & Karypis, 2016 ), which can be seen as a binary class problem ( Alamri et al, 2020 ), or on cumulative grade point average (GPA), which is a multi-class classification problem ( Adekitan & Salau, 2019 ). The regression approach predicts the numerical value of the grade ( Asif et al, 2017 ; Hunt-Isaak et al, 2020 ; Polyzou & Karypis, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses

Hidalgo Suarez,

Llanos,

Bucheli

2023

PeerJ Computer Science

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This article introduces a model for accurately predicting students’ final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model’s performance compared to existing literature models, highlighting its effectiveness.

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Predicting Student Academic Performance using Support Vector Machine and Random Forest

Cited by 25 publications

References 12 publications

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Performance Evaluation of Different Machine Learning Algorithms on Student Dataset Clustered by K-means Algorithm

Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses

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