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\documentclass[review]{elsarticle}
\usepackage{lineno,hyperref}
\usepackage{color}
\usepackage{url}
\usepackage{subfigure}
\usepackage{mathtools}
\usepackage{float}
\modulolinenumbers[5]
\journal{Journal of \LaTeX\ Templates}
%%%%%%%%%%%%%%%%%%%%%%%
%% Elsevier bibliography styles
%%%%%%%%%%%%%%%%%%%%%%%
%% To change the style, put a % in front of the second line of the current style and
%% remove the % from the second line of the style you would like to use.
%%%%%%%%%%%%%%%%%%%%%%%
%% Numbered
%\bibliographystyle{model1-num-names}
%% Numbered without titles
%\bibliographystyle{model1a-num-names}
%% Harvard
%\bibliographystyle{model2-names.bst}\biboptions{authoryear}
%% Vancouver numbered
%\usepackage{numcompress}\bibliographystyle{model3-num-names}
%% Vancouver name/year
%\usepackage{numcompress}\bibliographystyle{model4-names}\biboptions{authoryear}
%% APA style
%\bibliographystyle{model5-names}\biboptions{authoryear}
%% AMA style
%\usepackage{numcompress}\bibliographystyle{model6-num-names}
%% `Elsevier LaTeX' style
\bibliographystyle{elsarticle-num}
%%%%%%%%%%%%%%%%%%%%%%%
\newcommand{\todo}[1]{{\color{red} [#1]}}
\newcommand{\fodo}[1]{\todo{\footnote{\todo{#1}}}}
\newcommand{\todots}{\todo{\ldots}}
\newcommand{\totalStudents}{227~}
\newcommand{\hypRange}{72\%~}
\newcommand{\semesterPercentage}{22\%~}
\newcommand{\huxleyProblems}{400~}
\newcommand{\huxleyProfessors}{100~}
\newcommand{\huxleyCourses}{100~}
\newcommand{\huxleyInstitutions}{20~}
\newcommand{\higherPrecision}{76\%~}
\newcommand{\studentsCoursesAverage}{32~}
\newcommand{\semesters}{seven~}
\begin{document}
\begin{frontmatter}
\title{30 Days After Introducing Programming:\\Will My Students Fail?}
%\title{A Strategy to Early Identify Potential Failing Students in Introductory Programming Courses}
%\tnotetext[mytitlenote]{Fully documented templates are available in the elsarticle package on \href{http://www.ctan.org/tex-archive/macros/latex/contrib/elsarticle}{CTAN}.}
%% Group authors per affiliation:
%\author{M\'{a}rcio Ribeiro}
%\address{Radarweg 29, Amsterdam}
%% or include affiliations in footnotes:
\author[ufal]{M\'{a}rcio Ribeiro}
\ead{[email protected]}
\author[ufal]{Rodrigo Paes\corref{mycorrespondingauthor}}
\cortext[mycorrespondingauthor]{Corresponding author}
\ead{[email protected]}
\author[ufcg]{Rohit Gheyi}
\ead{[email protected]}
\address[ufal]{Federal University of Alagoas, Macei\'{o}, Brazil}
\address[ufcg]{Federal University of Campina Grande, Campina Grande, Brazil}
\begin{abstract}
Predictors to identify whether a student will succeed or fail in introductory programming courses have been provided by previous research. However, these predictors rely on big, complicated, and time-consuming aptitude tests and surveys, bringing subjectiveness due to the interview process. In addition, setting, executing, and replicating these studies is hard and increase the professor effort (especially in courses with many students). Other predictors---even though automatic to avoid this effort problem---either do not identify the failing students early or do not provide high precision. To minimize these problems, we propose a strategy to early predict the potential failing students during introductory programming courses automatically, reducing effort and allowing professors to use it in every course. By having this set of students in the first days of the course, professors and mentors would have time to act and potentially avoid such failings. The strategy consists of three simple steps: the use of an online judge system; the collection of metrics from this system; and the application of a clustering algorithm. To evaluate our strategy, we conduct an empirical study regarding \totalStudents freshmen students of \semesters introductory programming courses (3.5 years in total; \semesters semesters). We apply our strategy considering the first 30 days of the course (\semesterPercentage of each semester). The same professor taught all courses using the C language. The study reveals that, from the group of students our strategy points as ``likely to fail,'' \higherPrecision of the students indeed fail with 95\% standard confidence level. Although we face false positives, this set still seems interesting, since around 50\% of these students reach the final exam. Therefore, although they pass, we still consider they are good candidates to give special attention as well, which may avoid final exams and lead to better grades. We also conclude that the efficacy of our strategy depends on the number of students in the course: with few enrolled students, the own professor can identify the candidates to fail.
\end{abstract}
\begin{keyword}
Predictors, Introductory Programming Courses, Dropout Rate.
%\MSC[2010] 00-01\sep 99-00
\end{keyword}
\end{frontmatter}
\linenumbers
\input{sections/introduction.tex}
\input{sections/problem.tex}
\input{sections/strategy.tex}
\input{sections/evaluation.tex}
\input{sections/related.tex}
\input{sections/concluding.tex}
\section*{Acknowledgments}
We would like to thank CNPq, a Brazilian research funding agency, for partially supporting this work. Ribeiro's work was partially supported by RHAE 453716/2013-0.
\section{References}
\bibliography{mybibfile}
\end{document}