Mokpo National Maritime University Establishes AI-Based 'Early Dropout Warning System'

Mokpo National Maritime University campus view (Photo by Mokpo National Maritime University)

[Asia Economy Honam Reporting Headquarters Reporter Seo Young-seo] Mokpo National Maritime University (President Park Seong-hyun) announced on the 6th that it established an ‘AI-based Early Warning System for Student Dropout’ on the 28th of last month, which diagnoses and analyzes various factors of enrolled students to infer whether students will drop out.

The GPS Talent Education Center (Director Kim Nuri) developed this innovative system to overcome the university crisis caused by the outflow of local talent due to the decline in school-age population and the concentration of universities in the metropolitan area, and to advance a data-driven university education system.

The ‘AI-based Early Warning System for Student Dropout’ project began in August 2020, with system development over six months, followed by final testing and mock exam analysis before its establishment.

This system aims to infer dropout status by connecting student information through a convolutional neural network, converting student data into images, then performing machine learning on an artificial neural network, and finally predicting dropout status through the trained neural network.

Through this, Mokpo National Maritime University can predict student dropouts in advance starting with the 2021 academic year freshmen, and provide demand-tailored educational programs and other services to students predicted to drop out.

Mokpo National Maritime University plans to continuously operate this system to manage the quality of student education based on data, as well as to improve student retention rates, dropout rates, and satisfaction with university education services.

Park Seong-hyun, President of Mokpo National Maritime University, stated, “With the establishment of the AI-based early warning system for dropout, it is expected to be actively utilized to reduce the dropout rate of enrolled students by utilizing various data managed by each department within the university and to establish a system that strengthens personalized management capabilities for prevention.”

This content was produced with the assistance of AI translation services.