AI Hybrid Model for RBE Student Predictions

Published Date : 2/10/2025 

Explore how a hybrid AI model combining machine learning and deep learning techniques is being used to predict academic performance in Regular Basic Education (RBE) students, offering valuable insights for educational institutions. 

Machine learning has advanced significantly in recent years and is being used in higher education to perform various types of data analysis. While the literature demonstrates the application of machine learning algorithms to predict performance in university education, no such applications are found in Regular Basic Education (RBE), let alone in private institutions of a denominational nature. This presents an opportunity to study prediction in these institutions.

To address this gap, this research aims to propose a predictive approach as a decision-support tool for RBE, using machine learning techniques. Among the techniques utilized, three machine learning models (Logistic Regression, Support Vector Machine, and Random Forest), along with deep learning models (AlexNet, Gated Recurrent Unit, and Bidirectional Gated Recurrent Unit), were analyzed, as well as ensemble models.

The Ensemble model, which combines deep learning and machine learning techniques, is preferred due to its superior accuracy, precision, and sensitivity performance metrics. This model offers a comprehensive and robust solution for predicting academic performance, thereby aiding educators and administrators in making informed decisions.

The research was conducted by a team from Universidad Peruana Union, Universidad Nacional Toribio Rodriguez de Mendoza de Amazonas, and Quaid-i-Azam University. These institutions collaborated to develop and test the hybrid AI model, ensuring its effectiveness in real-world educational settings.

By leveraging the power of hybrid AI, this predictive approach can help identify students who may need additional support, tailored interventions, and personalized learning plans. This, in turn, can lead to improved academic outcomes and a more effective educational experience for RBE students.

The findings of this research highlight the potential of AI in transforming the educational landscape. It not only provides a valuable tool for educators but also opens up new avenues for further research and development in the field of educational technology.

In conclusion, the hybrid AI model proposed in this study represents a significant step forward in the use of advanced analytics for predicting academic performance in RBE students. Its successful implementation can have far-reaching implications for educational institutions, ultimately leading to better outcomes for students and more effective teaching practices. 

Frequently Asked Questions (FAQS): 

Q: What is Regular Basic Education (RBE)?

A: Regular Basic Education (RBE) refers to the educational system that provides foundational education to students, typically covering primary and secondary levels. It is a crucial stage in a student's academic journey, setting the groundwork for higher education and future career paths.

Q: Why is predicting academic performance important in RBE students?

A: Predicting academic performance in RBE students is important because it helps educators and administrators identify students who may need additional support or intervention. This can lead to more effective teaching practices and improved academic outcomes for students.

Q: What machine learning models were used in the study?

A: The study utilized three machine learning models: Logistic Regression, Support Vector Machine, and Random Forest. These models were chosen for their robustness and ability to handle complex data patterns.

Q: What deep learning models were analyzed in the research?

A: The research analyzed three deep learning models: AlexNet, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (BiGRU). These models are known for their ability to process sequential data and capture temporal dependencies.

Q: Why is the Ensemble model preferred in this study?

A: The Ensemble model is preferred because it combines the strengths of both machine learning and deep learning techniques, resulting in superior accuracy, precision, and sensitivity performance metrics. This makes it a more reliable and effective tool for predicting academic performance. 

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