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Diffstat (limited to 'aied2017/aied2017.tex')
| -rw-r--r-- | aied2017/aied2017.tex | 18 |
1 files changed, 16 insertions, 2 deletions
diff --git a/aied2017/aied2017.tex b/aied2017/aied2017.tex index d342a9c..5309c76 100644 --- a/aied2017/aied2017.tex +++ b/aied2017/aied2017.tex @@ -2,15 +2,26 @@ \usepackage[utf8]{inputenc} +\usepackage{fancyvrb} +\fvset{commandchars=\\\{\},baselinestretch=0.98,samepage=true,xleftmargin=5mm} + +\usepackage{forest} + +\newcommand\code[1]{\texttt{#1}} +\newcommand\red[1]{{\begingroup\color[rgb]{0.8,0.15,0.15}#1\endgroup}} +\newcommand\hl[1]{\textbf{#1}} + \begin{document} -\title{TODO} +\title{Patterns for debugging student programs} \author{TODO} \institute{University of Ljubljana, Faculty of Computer and Information Science, Slovenia} \maketitle \begin{abstract} -TODO +We propose new program features to support mining data from student submissions in a programming tutor. We extract syntax-tree patterns from student programs, and use them as features to induce rules for predicting program correctness. Discovered rules allow us to correctly classify a large majority of submissions based only on their structural features. Rules can be used to recognize intent, and provide hints in a programming tutor by pointing out incorrect or missing patterns. Evaluating out approach on past student data, we were able to find errors in over 80\% of incorrect submissions. +\\\\ +\textbf{Keywords:} Intelligent tutoring systems · Programming · Hint generation \end{abstract} \input{introduction} @@ -20,4 +31,7 @@ TODO \input{evaluation} \input{conclusion} +\bibliographystyle{splncs} +\bibliography{aied2017} + \end{document} |