Deep Learning-Infused Cascading Regression Approach to Predict the Academic Performance of Immigrant Students
Keywords:
Academic performance, Bi-directional RNN, CatBoost, Deep Learning, Immigrant students, PISA dataset, Random Forest.Abstract
The academic performance of immigrant students is governed by a diverse range of resources and contexts, including the families of the students, the immigrant communities from which the students originate, and the social and educational attitudes that are held toward immigrants in the countries in which the students are currently residing. The Program for International Student Assessment, is an educational research initiative that is used to assess the knowledge and skills of students who are 15 years old. In this paper, the performance of immigrant students is predicted using the PISA dataset. There are a total of 35 attributes present in the dataset. Among these, the proposed method chooses three attributes(maths, science and reading) as target variables for performance prediction. This research constitutes a novel cascading regression framework designed to accurately forecast academic performance. Sequentially integrating CatBoost Regressor, Bidirectional Recurrent Neural Network (Bi-RNN), and Random Forest Meta Regressor optimizes predictive accuracy. Initiated by the CatBoost Regressor, its outputs serve as inputs for a Bi-RNN model, exploiting bidirectional sequential information. The ensuing predictions from Bi-RNN inform a Random Forest Meta-Regressor, refining the final outcome. Evaluation metrics, comprising MAPE, RMSE, and R2, substantiate the superior accuracy of the cascading model. The cascading ensemble significantly outperformed all individual models, achieving a MAPE reduction of 3.74%, an RMSE reduction of 20.70%, and an R-squared increase of 0.96.This research highlights the efficacy of cascading ensemble techniques for predicting student performance with enhanced accuracy. The method being proposed demonstrates the capacity to capture both fixed and changing characteristics, which may result in enhanced interventions and educational decision-making.
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