First, the proposed approach is based on an ensemble of three Fuzzy ARTMAPs (FMAPs). Second, the decision is based on three risk levels (Low, Medium, High).

This study describes a neural networks-based framework for predicting undergraduate students at risk of dropout in higher education, in the traditional face-to- ...

Jul 16, 2021 · A framework for predicting students at risk of dropout in Higher Education Institutions is presented. The approach differs from the existing ...

A framework for predicting students at risk of dropout in Higher Education Institutions is presented based on an ensemble of three Fuzzy ARTMAPs (FMAPs), ...

Apr 25, 2024 · https://dblp.org/rec/conf/ijcnn/Murshed21. Nabeel A. Murshed: A Fuzzy ARTMAP Framework for Predicting Student Dropout in Higher Education.

A framework for predicting students at risk of dropout in Higher Education Institutions is presented. The approach differs from the existing ones in three ...

Machine learning is a promising tool for building a predictive model for student dropout and offers early warning to responsible authorities to take alternative ...

Jun 9, 2020 · We developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student.

One of the main concerns of online learning is high dropout rate. The dropout rates for online education are generally higher than conventional education.

Early detection of students at risk - Predicting student dropouts using administrative student data from German Universities and machine learning methods.