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Copy file name to clipboardExpand all lines: Data-Driven Admissions in Education: Enhancing Student Success by Matching Profiles to Optimal Academic Paths.tex
abstract = {Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.},
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booktitle = {Wikipedia},
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urldate = {2023-12-14},
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date = {2023-08-10},
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langid = {english},
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note = {Page Version {ID}: 1169729264},
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}
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@inreference{noauthor_random_2023,
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title = {Random forest},
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rights = {Creative Commons Attribution-{ShareAlike} License},
abstract = {Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set.: 587–588 The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.An extension of the algorithm was developed by Leo Breiman and Adele Cutler, who registered "Random Forests" as a trademark in 2006 (as of 2019, owned by Minitab, Inc.). The extension combines Breiman's "bagging" idea and random selection of features, introduced first by Ho and later independently by Amit and Geman in order to construct a collection of decision trees with controlled variance.},
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booktitle = {Wikipedia},
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urldate = {2023-12-14},
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date = {2023-12-11},
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langid = {english},
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note = {Page Version {ID}: 1189363809},
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}
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@misc{noauthor_donnees_2022,
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title = {Données relatives à l’épidémie de {COVID}-19 en France : vue d’ensemble},
title = {Statistiques sur les effectifs d'étudiants inscrits par établissement public sous tutelle du ministère en charge de l'Enseignement supérieur (avec doubles inscriptions {CPGE})},
author = {Taplin, Dr. Dana H. and Collins, Eoin and Clark, Dr. Heléne and Colby, David C.},
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urldate = {2023-10-31},
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date = {2013-04},
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}
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@book{ahrne_meta-organizations_2008,
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title = {Meta-organizations},
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isbn = {978-1-84844-265-8},
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abstract = {A growing number of organizations are meta-organizations; rather than individuals they have other organizations as their members. This comprehensive book explains, in-depth, the unique way in which meta-organizations function, how they differ from organizations with individual membership, and how they are crucial agents in the process of globalization.},
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pagetotal = {201},
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publisher = {Edward Elgar Publishing},
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author = {Ahrne, Göran and Brunsson, Nils},
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date = {2008-01-01},
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langid = {english},
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keywords = {Business \& Economics / Management},
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}
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@article{gulati_meta-organization_2012,
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title = {Meta-organization design: Rethinking design in interorganizational and community contexts},
abstract = {In a seminal paper, Ahrne and Brunsson coined the word ‘meta-organization’. More than a label, this word describes a challenging and stimulating concept that can be valuable for management studies when approached with different units of analysis and research methodologies. ‘Meta-organization’ refers to a central phenomenon in the contemporary world, namely the increasing importance of collective action at the level of organizations, ensuing from major issues related to sustainable development, human rights and corporate responsibility. The concept calls for new forms of theorizing of global collective action. The diversity and heterogeneity of meta-organizations raise methodological issues that require original approaches. In this paper, we show the novelty of the concept of meta-organization; we then address the methodological difficulties and propose a research agenda on meta-organizations for management studies.},
abstract = {This guidance note looks at what an impact-oriented M\&E system involves, and when might it be useful to establish one},
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titleaddon = {{ODI}: Think change},
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author = {Hearn, Simon and Pasanen, Tiina and Buffardi, Anne},
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urldate = {2023-11-11},
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date = {2016-03-07},
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langid = {english},
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}
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@online{unita_unita_nodate,
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title = {{UNITA} - Universitas Montium},
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url = {http://www.univ-unita.ubi.pt},
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abstract = {{UNITA} - We are an alliance of six comprehensive research universities from five countries with different sizes and trajectories gathering together more than 160 000 students and 13 000 staff members.},
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titleaddon = {{UNITA} - Universitas Montium},
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author = {{UNITA}},
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urldate = {2023-11-11},
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langid = {portuguese},
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}
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@article{kuh_what_2006,
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title = {What Matters to Student Success: A Review of the Literature},
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.
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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.
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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.
In particular, trees that are grown very deep tend to learn highly irregular patterns: they overfit their training sets, i.e. have low bias, but very high variance.
We will now do an analysis from the literature review on how we can approach the problem, using what's been already made and how we can improve on it.
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\subsection{Factors}
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First of all, what differentiate this research from all the other we have read throughout the literature analysis is that we are not seeking prediction on student's dropout but rather on student success and early in the process and not during the curriculum year. However, there is plenty of interesting information we can gather from these papers. As described in the \ref{sec:soa} State of Art, we can gather factors that, in theory could help predict student's dropout. We can hypothesize that by using these factors to determine if one student is at risk of dropping-out, it could for another predict its success in a specific formation. From the list of factors we were able to gather, we have made a statistical analysis of the frequency they appear and their overall score within each paper they are mention it. Below, the table from this study concluding our research.
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\subsection{Machine Learning algorithm}
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Secondly, we need to understand which algorithm model have been used the most and which present the best outcome for our need. As for the factors, we can extrapolate the problem and take it in reverse. So by learning which algorithm presents the best result to predict student's dropout, we could hypothesize that they could also be used to detect student's success.
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\subsection{Analysis conclusion}
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Both our hypothesis and result must now be verified by providing a methodology and using a test dataset to send to our pipeline in order to feed our machines.
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We may find that one or both hypothesis are not correct and we will need to restudy factors and machine learning algorithm to answer our need and problematic.
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In the next part, \ref{sec:conceptualanalysis} Conceptual implementation, we are going to present our methodology and workflow. Explaining the reasons for our choice of factors and algorithm as well as presenting our entire pipeline for our system.
We now need to create our pipeline and workflow before we can start building it. The questions we need to answer are : what data are we going to feed into the pipeline and which algorithm are we going to feed?
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It is clear that by the results from our state of the art and analysis that not one algorithm must be used in our workflow to achieve the best result.
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To extract as much information and get the possible best results, we have split our system into three inner parts, each with their responsibility, input and output.
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But first, let's look into which data we have access to and what to use to feed our system.
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