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diff --git a/paper/aied2017.tex b/paper/aied2017.tex index 3faaaf8..3acc90e 100644 --- a/paper/aied2017.tex +++ b/paper/aied2017.tex @@ -39,13 +39,13 @@ \begin{abstract} % motivation -When implementing a programming tutor, it is often difficult to manually consider all possible errors encountered by students. An alternative is to automatically learn a bug library of erroneous patterns from students’ programs. +When implementing a programming tutor it is often difficult to manually consider all possible errors encountered by students. An alternative is to automatically learn a bug library of erroneous patterns from students’ programs. % learning -We propose using abstract-syntax-tree (AST) patterns as features for learning rules to distinguish between correct and incorrect programs. These rules can be used for debugging student programs: rules for incorrect programs (buggy rules) contain patterns indicating mistakes, whereas rules for correct programs cover subsets of submissions sharing the same solution strategy. +We propose abstract-syntax-tree (AST) patterns as features for learning rules to distinguish between correct and incorrect programs. We use these rules to debug student programs: rules for incorrect programs (buggy rules) indicate mistakes, whereas rules for correct programs group programs with the same solution strategy. % generating hints -To generate hints, we first check all buggy rules and point out incorrect patterns. If no buggy rule matches, rules for correct programs are used to recognize the student’s intent and suggest patterns that still need to be implemented. +To generate hints, we first check buggy rules and point out incorrect patterns. If no buggy rule matches, we use rules for correct programs to recognize the student’s intent and suggest missing patterns. % evaluation -We evaluated our approach on past student programming data for a number of Prolog problems. For 31 out of 44 problems, the induced rules correctly classified over 85\% of programs based only on their structural features. For approximately 73\% of incorrect submissions, we were able to generate hints that were implemented by the student in some subsequent submission. +We evaluate our approach on past student programming data for a number of Prolog problems. For 31 out of 44 problems, the induced rules correctly classify over 85\% of programs based only on their structural features. For approximately 73\% of incorrect submissions, we are able to generate hints that were implemented by the student in some subsequent submission. \\\\ \textbf{Keywords:} Programming tutors · Error diagnosis · Hint generation · Abstract syntax tree · Syntactic features \end{abstract} |