Introduction

Author: Ttiki

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

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 \vspace{16pt}
 
 \section{Introduction}
-In today's rapidly evolving educational landscape, optimizing the admission process in higher education institutions has never been more critical. With the aftermath of the COVID-19 pandemic and the surge in students seeking higher education, universities face the daunting task of managing a vast number of admissions while ensuring they admit candidates best suited for their programs.
+In today's rapidly evolving educational landscape, optimizing the admission process in higher education institutions has never been more critical. With the aftermath of the COVID-19 pandemic and the surge in students seeking higher education, universities face the daunting task of managing a vast number of admissions while ensuring they admit candidates best suited for their programs. It is estimated that for the year 2021-2022, 2.97 millions students have registered for higher education.\cite{sous-direction_des_systemes_dinformation_et_des_etudes_statistiques_sies_les_2022} 
 
 The Parcoursup system, introduced as a centralized platform for higher education admissions in France, aimed to streamline this process. While it brought some level of uniformity and transparency, criticisms have arisen regarding its limitations in truly identifying candidates' aptitude and ensuring a match between a student's potential and the chosen program.
 
+\begin{table}
+    \centering
+    \caption{Results in Bachelor's degree for the 2013 and 2014 sessions for students enrolled for the first time in the first year of Bachelor's in 2010-2011.\cite{kabla-langlois_fporsoc16b_ec2_enseignementpdf_2016}}
+    \begin{tabular}{|c|c|}
+        \hline
+         \textbf{Headcount 2010} & \textbf{Réussite cumulée en 4 ans (en \%)} \\
+         \hline
+         169 652 & 39.8 \\
+         \hline
+    \end{tabular}
+    
+    \label{tab:my_label}
+\end{table}
+
 Enter the realm of machine learning and data analytics, technologies that promise a more nuanced and accurate approach to admissions. By analyzing a myriad of factors beyond traditional metrics, this approach seeks to identify candidates whose profiles align perfectly with specific programs, thereby increasing the likelihood of academic success.
 
 This research endeavors to explore and validate the potential of machine learning and data analytics in revolutionizing the admission process. The motivation is twofold: to enhance the success rate of students by ensuring they are placed in programs where they are most likely to excel and to reduce dropout rates by minimizing mismatches between students and programs.

references.bib

@@ -1,4 +1,92 @@
 
+@misc{kabla-langlois_fporsoc16b_ec2_enseignementpdf_2016,
+	title = {{FPORSOC}16b\_EC2\_enseignement.pdf},
+	publisher = {{INSEE} Références},
+	author = {Kabla-Langlois, Isabelle},
+	urldate = {2023-11-06},
+	date = {2016},
+	langid = {french},
+}
+
+@article{bodin_question_2011,
+	title = {La question de l'« abandon » et des inégalités dans les premiers cycles à l'université},
+	volume = {17},
+	issn = {1958-7856},
+	url = {https://www.cairn.info/revue-savoir-agir-2011-3-page-65.htm},
+	doi = {10.3917/sava.017.0065},
+	pages = {65--73},
+	number = {3},
+	journaltitle = {Savoir/Agir},
+	shortjournal = {Savoir/Agir},
+	author = {Bodin, Romuald and Millet, Mathias},
+	urldate = {2023-11-06},
+	date = {2011},
+	langid = {french},
+	note = {Place: Vulaines-sur-Seine
+Publisher: Éditions du Croquant},
+}
+
+@online{sous-direction_des_systemes_dinformation_et_des_etudes_statistiques_sies_les_2022,
+	title = {Les effectifs d’étudiants dans le supérieur continuent leur progression en 2021-2022},
+	url = {https://www.enseignementsup-recherche.gouv.fr/fr/les-effectifs-d-etudiants-dans-le-superieur-continuent-leur-progression-en-2021-2022-88609},
+	abstract = {En 2021-2022, 2,97 millions d’inscriptions ont été enregistrées dans l’enseignement supérieur français. En augmentation depuis la rentrée 2008, le nombre d’étudiants progresse de 2,5 \% à la rentré 2021 par rapport à la rentrée précédente et de 2,2 \% par an en moyenne depuis 5 ans.},
+	titleaddon = {enseignementsup-recherche.gouv.fr},
+	author = {Sous-direction des systèmes d'information et des études statistiques ({SIES}) and Ministère de l'Enseignement supérieur et de la Recherche},
+	urldate = {2023-11-06},
+	date = {2022-12-20},
+	langid = {french},
+}
+
+@article{murphy_impact_2013,
+	title = {Impact of student choice on academic performance: cross-sectional and longitudinal observations of a student cohort},
+	volume = {13},
+	issn = {1472-6920},
+	url = {https://doi.org/10.1186/1472-6920-13-26},
+	doi = {10.1186/1472-6920-13-26},
+	shorttitle = {Impact of student choice on academic performance},
+	abstract = {Student choice plays a prominent role in the undergraduate curriculum in many contemporary medical schools. A key unanswered question relates to its impact on academic performance.},
+	pages = {26},
+	number = {1},
+	journaltitle = {{BMC} Medical Education},
+	shortjournal = {{BMC} Med Educ},
+	author = {Murphy, Michael J. and Seneviratne, Rohini {DeA} and Cochrane, Lynda and Davis, Margery H. and Mires, Gary J.},
+	urldate = {2023-11-05},
+	date = {2013-02-19},
+	langid = {english},
+	keywords = {Academic Performance, Generalise Estimate Equation, Objective Structure Clinical Examination, Summative Assessment, Undergraduate Curriculum},
+}
+
+@article{hoxby_how_2009,
+	title = {{HOW} {NEW} {YORK} {CITY}'S {CHARTER} {SCHOOLS} {AFFECT} {ACHIEVEMENT}},
+	author = {Hoxby, Caroline M and Murarka, Sonali and Kang, Jenny},
+	date = {2009},
+	langid = {english},
+}
+
+@online{rashid_effects_2023,
+	title = {The Effects of School Choice on Student Achievement {\textbar} limbd.org},
+	url = {https://limbd.org/the-effects-of-school-choice-on-student-achievement/},
+	abstract = {The Effects of School Choice on Student Achievement have been a topic of significant debate and research in the field of education.},
+	titleaddon = {Library \& Information Management},
+	author = {Rashid, Md Harun Ar},
+	urldate = {2023-11-05},
+	date = {2023-05-30},
+	langid = {american},
+}
+
+@report{kroc_graduation_1997,
+	title = {Graduation Rates: Do Students' Academic Program Choices Make a Difference?},
+	url = {https://eric.ed.gov/?id=ED417677},
+	shorttitle = {Graduation Rates},
+	abstract = {This study looked at the relationship between the programs students chose upon college entry, the programs from which they graduated, and the time taken to graduate. Individual student data on more than 204,000 freshmen entering 38 public, land grant, and Research I universities in 1988 and 1990 were collected. Descriptive statistics were used to summarize student entry characteristics such as college admission test scores and high school grade point averages. Logistic analysis was used to calculate and compare predicted and actual graduation rates based on student entry characteristics. Among findings were: graduation rates varied more by university than by program; time to completion varied by academic program; business and social sciences were the program areas which  experienced the largest in-migration from other areas; students entering education programs had the lowest average Scholastic Assessment Test scores whereas engineering students had the highest; students initially undecided about their major were no less likely to graduate than other students and their graduation was not delayed; there was a correlation of about .28 between actual and predicted graduation rates; and there were significant differences among institutions between actual graduation rates and predicted graduation rates. Tables provide detailed findings for each institutions. ({DB})},
+	author = {Kroc, Rick and Howard, Rich and Hull, Pat and Woodard, Doug},
+	urldate = {2023-11-05},
+	date = {1997-05},
+	langid = {english},
+	note = {{ERIC} Number: {ED}417677},
+	keywords = {College Freshmen, Course Selection (Students), Decision Making, Diversity (Institutional), Graduation, Higher Education, Land Grant Universities, Majors (Students), Prediction, Predictor Variables, Research Universities, State Universities, Student Characteristics, Time to Degree},
+}
+
 @article{kritsonis_comparison_nodate,
 	title = {Comparison of Change Theories},
 	abstract = {The purpose of this article is to summarize several change theories and assumptions about the nature of change. The author shows how successful change can be encouraged and facilitated for long-term success. The article compares the characteristics of Lewin’s Three-Step Change Theory, Lippitt’s Phases of Change Theory, Prochaska and {DiClemente}’s Change Theory, Social Cognitive Theory, and the Theory of Reasoned Action and Planned Behavior to one another. Leading industry experts will need to continually review and provide new information relative to the change process and to our evolving society and culture.},