Year: 2024
Type: article
Abstract: This work created an improved deep learning model for predicting student academic achievement. Students' data is gathered from online learning platforms, offline learning systems in schools, or through questions and responses. Many writers could use the collected data to better understand student behavior and, as a result, improve learning levels and student performance. To serve as legitimate input for a deep learning model, the gathered data must be processed first. The deep learning model's hyper-parameters were optimized using a genetic method. The OULAD dataset was utilized for validation. The results demonstrated that CNN plus a genetic algorithm is an effective strategy for predicting academic success.
Author: Mina Basheer Gheni
Institution: Nahrain University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4405152998