An analysis of students’ performance using CART approach

Nawai, Siti Noraliyah Mohd; Saharan, Sabariah; Hamzah, Nur Atiqah

doi:10.1063/5.0053388

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…DT shows the highest predictive accuracy among the DM models, with an accuracy of 87.1%, followed by SVM, NB, and NN. This finding is consistent with findings by Nawai et al (2021) who found that DT surpassed other DM classifiers in terms of predictive performance. It may be argued that DT is the most dominant classifier since it creates classification rules that apply to both nominal and categorical data (Shetu et al, 2021).…”

Section: Predictive Model With the Highest Accuracysupporting

confidence: 93%

Predicting students’ performance in English and Mathematics using data mining techniques

Roslan

Chen

2022

Educ Inf Technol

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This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

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Section: Predictive Model With the Highest Accuracysupporting

confidence: 93%

Predicting students’ performance in English and Mathematics using data mining techniques

Roslan

Chen

2022

Educ Inf Technol

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“…Decision trees, commonly employed in machine learning, are adept at tackling regression and classification issues. In classification, the aim is to forecast a label, whereas in regression, it forecasts a quantity [20]. The predictive model was implemented to anticipate the target variable-course evaluation.…”

Section: Decision Tree Algorithm Figure 2 Decision Tree Algorithmmentioning

confidence: 99%

Mobile-Web Responsive Portal for St. John Paul II College of Davao Applying Predictive Analytics Techniques

2024

IJATCSE

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This study represents the portal for St. John Paul II College of Davao (SJP2CD). It is a responsive system to mobile and web platforms that enables the school community to operate efficiently throughout enrollment and enhances online access to academic information. The portal is built with Django and Python to create effective and timely management information and the database used is SQLite. Furthermore, the Classification and Regression Tree (CART) model is used for the automatic advising for the student enrollment process and the Linear Regression Model for the enrollment prediction. This portal is accessible via web browsers and mobile devices. During the deployment phase, when all required assessments are finished through extensive functionality testing and how the portal is helpful to all users, the development sprints’ goals were effectively achieved. In conclusion, the SJP2CD Portal has proved to meet all users' needs.

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Enhancing Student Success Prediction with FeatureX: A Fusion Voting Classifier Algorithm with Hybrid Feature Selection

Malik,

Jothimani

2023

Educ Inf Technol

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An analysis of students’ performance using CART approach

Cited by 7 publications

References 19 publications

Predicting students’ performance in English and Mathematics using data mining techniques

Predicting students’ performance in English and Mathematics using data mining techniques

Mobile-Web Responsive Portal for St. John Paul II College of Davao Applying Predictive Analytics Techniques

Enhancing Student Success Prediction with FeatureX: A Fusion Voting Classifier Algorithm with Hybrid Feature Selection

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