A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk.

ABSTRACT:

Background

Student dropout rates are one of the major concerns of educational institutions because they affect the success and efficacy of them. In order to help students continue their learning and achieve a better future, there is a need to identify the risk of student dropout. However, it is challenging to accurately identify the student dropout risk in the preliminary stages considering the complexities associated with it. This research develops an efficient prediction model using machine learning (ML) and deep learning (DL) techniques for identifying student dropouts in both small and big educational datasets.

Methods

A hybrid prediction model DeepS3VM is designed by integrating a Semi-supervised support vector machine (S3VM) model with a recurrent neural network (RNN)

SUBMITTER: Nguyen Thi Cam H 

PROVIDER: S-EPMC11623006 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature