Data-Driven Admissions in Education: Enhancing Student Success by Matching Profiles to Optimal Academic Paths.
This is my first thesis for the Master SIGLIS diploma from the Université de Pau et des Pays de l'Adour.
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 research endeavours to address a critical issue within the education system, offering a more holistic and personalized approach to student placement. By reimagining the criteria for admission, we aspire to create a more harmonious and productive educational landscape for both students and academic institutions.
Higher education, Admission process, Machine learning, Data analytics, Success rate, Dropout rate, Student profile, Optimization, Profile-degree matching, Admission management, High-achieving students, Adaptive education