v1.0.0

[Ttiki]

This is the final version uploaded on the EasyChair platform for the final submission. Some minor update shall be pushed for minor changes before the closure of the platform.

Title : Data-Driven Admissions in Education: Enhancing Student Success by Matching Profiles to Optimal Academic Paths

Abstract : In the wake of the COVID-19 pandemic and the release of the new baccalaureate reform, French education authorities in higher studies faces a surge of enrolments and higher dropouts numbers. Higher grade from students in the baccalaureate as lead, the French registration system in place to accept more and more students in higher degrees paths. Sadly, these new reforms did not take into account the difficulty step created between secondary and higher studies. Thus augmenting the number of dropouts in students who don’t have the capacity, motivation and/or will to continue in their path. We propose a solution to mitigate this dropout as well as helping academia to find excellence students with compatible profile for a certain path (diploma and domain). Taking the problem at its root could lead to a two birds with one stone resolution to the problem. This paper focuses on critical issues within the education system and tries to differ a more holistic and personalized approach to student placement. By using data mining, analytic and machine learning, we hope to create a more harmonious and productive education landscape for both students and academic alike.

Full Changelog: v0.3.1...v1.0.0

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This discussion was created from the release v1.0.0.