Improving Math Proficiency Prediction in Computer-Based International Large-Scale Assessments with Data Augmentation | IEEE Conference Publication | IEEE Xplore

Improving Math Proficiency Prediction in Computer-Based International Large-Scale Assessments with Data Augmentation


Abstract:

This study explores the possibility to improve students' math proficiency prediction with a multi-class neural network model by augmenting the training dataset with synth...Show More

Abstract:

This study explores the possibility to improve students' math proficiency prediction with a multi-class neural network model by augmenting the training dataset with synthetic data. The original training dataset was based on the publicly released PISA 2012 computer-based database. Three math proficiency classes were established: low, mediocre and high. Minority class with the least number of samples was the high proficiency class. SMOTE, VAE and CTGAN methods were used to augment the minority class with additional data samples. G-mean was used as a performance measure for observing the enhancement to the prediction of the minority class.
Date of Conference: 15-17 September 2022
Date Added to IEEE Xplore: 13 February 2023
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Conference Location: Subotica, Serbia

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