Updates from Overleaf

Author: Ttiki

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

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 \author{\IEEEauthorblockN{Clément Combier}
 \IEEEauthorblockA{\textit{Master 2 SIGLIS} \\
 \textit{Université de Pau et des Pays de l'Adour}\\
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 \begin{abstract}
-In the wake of the COVID-19 pandemic and the release of the new \textit{baccalaureat} 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. 
+In the wake of the COVID-19 pandemic and the release of the new \textit{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 \textit{excellence students} with compatible profile for a certain path (diploma and domain). Taking the problem at its root could lead to a \textit{two birds with one stone} resolution to the problem.