Utilizing random forest algorithm for early detection of academic underperformance in open learning environments.

ABSTRACT:

Background

One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support.

Method

The specific objective of this study was to build a model that identifies at-risk students

SUBMITTER: Balabied SAA 

PROVIDER: S-EPMC10703007 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature