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\documentclass[final]{beamer}
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\title{Syntax-based analysis of programming concepts in Python}
\author{Martin Možina \& Timotej Lazar}
\institute{University of Ljubljana, Faculty of Computer and Information Science, Slovenia}
\def\myemail{$\lbrace$martin.mozina,timotej.lazar$\rbrace$@fri.uni-lj.si}
\def\mywebpage{https://ailab.si/ast-patterns}\titlegraphic{img/FRI_logo_eng_zaNogo.png}
\begin{document}
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\setbeamercolor*{block title}{fg=white,bg=FRIRed}
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\begin{myblock}{Motivation and Research Questions}
\input{motivation.tex}
\end{myblock}
\setbeamercolor*{block title}{fg=white,bg=TitleBG}
\begin{myblock}{AST patterns}
\input{patterns.tex}
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\setbeamercolor*{block title}{fg=white,bg=abstract}
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\begin{abstractblock}
\textbf{Abstract}
Writing programs is essential to learning programming. Most programming courses encourage students to practice with lab and homework assignments. By analyzing solutions to these exercises teachers can discover mistakes and concepts students are struggling with, and use that knowledge to improve the course. Students however tend to submit many different programs even for simple exercises, making such analysis difficult.
We propose using tree regular expressions to encode common patterns in programs. Based on these patterns we induce rules describing common approaches and mistakes for a given assignment. In this paper we present a case study of rule-based analysis for an introductory Python exercise. We show that our rules are easy to interpret, and can be learned from a relatively small set of programs.
\end{abstractblock}\vfill
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\begin{myblock}{Learning Rules and Results}
\input{rules.tex}
\end{myblock}\vfill
\setbeamercolor*{block title}{fg=white,bg=FRIRed}
\begin{myblock}{Conclusions}
\begin{itemize}
\item Abstract-syntax-tree (AST) patterns for representing program patterns.
\item Patterns are extracted automatically and combined into n-rules(errors) and p-rules (approaches) with machine learning.
\item Patterns are useful, because in our experiment ...
\begin{itemize}
\item classification accuracy of Random Forest was on average 17\% higher than default accuracy.
\item n-rules explained over 70\% of incorrect submissions.
\item p-rules explained 62\% of correct programs.
\end{itemize}
\item However ...
\begin{itemize}
\item In some domains, patterns were not informative (\textsf{ballistics} and \textsf{minimax}), therefore more sophisticated patterns are needed.
\item To construct new patterns, a tool for vizualization of patterns is needed.
\end{itemize}
\end{itemize}
\end{myblock}\vfill
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