Predicting Student Academic Performance Using Learning Vector Quantization and Probabilistic Neural Network
Year: 2026
Type: article
Abstract: Early prediction of student performance is an important task in the field of education, as it helps teachers identify the performance of the students and provide them with the required academic support. This paper proposes a framework based on Artificial Neural Network (ANN) techniques, namely Learning Vector Quantization (LVQ) and Probabilistic Neural Network (PNN), for the classification of the students based on the behavioral, academic, and demographic features of the students. The performance of the students is determined by using the historical data of the students. The proposed framework is based on the Streamlit platform, which is used for the visualization of the performance of the students.
Authors: Omkar Deshmukh, Yash Mishra, Sweta Nigam
Institution: University of Mumbai
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W7140134015
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