Ablation study on feature selection methods.

<div><p>Predicting student performance is crucial for providing personalized support and enhancing academic performance. Advanced machine-learning approaches are being used to understand student performance variables as educational data grows. A big dataset from several Chinese instituti...

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Bibliographic Details
Main Author:	Meshari Alazmi (452881) (author)
Other Authors:	Nasir Ayub (17200014) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified several chinese institutions recursive feature eliminatio providing personalized support predicting student performance performance level demonstrated logarithmic loss relative improved normalization methods enhancing academic performance confusion matrices display combined correlation filtering current research provides effixnet framework offers directed training support full test dataset developed model showed 8 %, f1 educational data grows educational research developed model big dataset 1 %, test split test results proposed effixnet effixnet value educational domain various metrics tested using study develops stability selection results reported represents one representative subset reliable optimization refined version potential applications mutual information missing values memory usage learning approaches key insights including accuracy impactful features hyperparameter optimization higher education high schools efficientnet augmented efficient solution early intervention dynamic convolutions
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Ablation study on feature selection methods.

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