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\section{Introduction}
% why automatic feedback
Programming education is becoming increasingly accessible with massive online courses. Since thousands of students can attend such courses, it is impossible for teachers to individually evaluate each participant’s work. On the other hand, in-time feedback directly addressing students’ mistakes can aid the learning process. Providing feedback automatically could thus greatly enhance these courses.
% ITS background
Traditional programming tutors use manually constructed domain models to generate feedback. Model-tracing tutors simulate the problem-solving \emph{process}: how students program. This is challenging because there are no well-defined steps when writing a program. Many tutors instead only analyze individual programs submitted by students, and disregard how a program evolved. They often use models coded in terms of constraints or bug libraries~\cite{keuning2016towards}.
% data-driven domain modeling
Developing a domain model requires significant knowledge-engineering effort~\cite{folsom-kovarik2010plan}. This is particularly true for programming tutors, where most problems have several alternative solutions with many possible implementations~\cite{le2013operationalizing}. Data-driven tutors reduce the necessary effort by mining educational data -- often from online courses -- to learn common errors and generate feedback~\cite{rivers2015data-driven,nguyen2014codewebs,jin2012program}.
% problem statement
In this paper we address the problem of finding useful features to support data mining in programming tutors. To support hint generation, these features must be robust against irrelevant code variations (such as renaming a variable) and relatable to knowledge components of the target skill (programming).
% our approach: patterns + rules
We describe features with \emph{abstract-syntax-tree patterns} that encode relations between nodes in a program’s abstract syntax tree. We use patterns that describe a path between pairs of leaf nodes referring to variables or values. By omitting some nodes on these paths, patterns can match different programs containing the same relation. We then induce rules to predict program correctness from AST patterns, allowing us to generate hints based on missing or incorrect patterns.
% evaluation
We evaluated our approach on existing Prolog programs submitted by students during past lab sessions of a second-year university course. For 73\% of incorrect submissions we are able to suggest potentially useful patterns -- those that the students had actually implemented in the final, correct programs.
% contributions
The main contributions presented in this paper are: AST patterns as features for machine learning, a rule-based model for predicting program correctness, and hint generation from incorrect or missing patterns in student programs.
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