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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}

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		\begin{myblock}{Motivation}
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		\begin{myblock}{AST patterns}
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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.
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			\begin{myblock}{Rules and results}
			\input{rules.tex}
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		\begin{myblock}{Conclusions}
			\begin{itemize}
				\item AST patterns represent program features, while induced n-rules and p-rules encode errors and solution strategies.

				\item Patterns are useful, because in our experiment ...\\
                                        ~~... classification accuracy was on average 17\% higher using patterns;\\
					~~... n-rules explained over 70\% of incorrect submissions;\\
					~~... p-rules explained 62\% of correct submissions.

				\item However ...\\
					~~... patterns were not always informative -- new kinds of patterns are needed;\\
					~~... a visualization tool could support discovery of new kinds of useful patterns.
			\end{itemize}
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