ML Model to Improve Learning process and Reduce Dropout Rates
This Research to Practice Full Paper presents a systematic review of methodologies that propose ways of reducing the dropout rate in Virtual Learning Environments (VLE). This generates large amounts of data about courses and students, whose analysis requires the use of computational analytical tools. Most educational institutions claim that the greatest issue in virtual learning courses is high student dropout rates.
Distance education has surged with the gradual progress in information technology over the past decade and the Scholar Dropout phenomenon has been increasing, having repercussions in social, economic, and academic aspects, among others. These changes have created new challenges for different stakeholders in managing the learning process through a virtual platform.
Scope of The Project:
Our study aims to identify solutions that use Machine Learning (ML) techniques to reduce these high dropout rates.
The knowledge discovered may help improve teaching/learning processes
A Virtual Learning Environment (VLE) is a virtual classroom that allows teachers and students to communicate with each other online.
These changes have created new challenges for different st ML Model to Improve Learning process and Reduce Dropout Rates
- Hardware Requirement:
- OS? Windows 7, 8, and 10 (32 and 64 bit)
- RAM ? GB
- Programming Language: