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diff --git a/aied2018/rules.tex b/aied2018/rules.tex index 572d261..c60b989 100644 --- a/aied2018/rules.tex +++ b/aied2018/rules.tex @@ -2,7 +2,7 @@ \label{sec:rules} \subsection{The learning algorithm} -The goal of learning rules in this paper is to extract and explain common approaches and mistakes in student programs. We use a rule learner called ABCN2e implemented within the Orange data mining library~\cite{demsar2013orange}. ABCN2e is an improvement of the classical CN2 algorithm~\cite{clarkECML1991} for learning unordered rules. The differences between CN2 and ABCN2e are described in a technical report found at \url{https://ailab.si/abml.} +The goal of learning rules in this paper is to extract and explain common approaches and mistakes in student programs. We use a rule learner called ABCN2e implemented within the Orange data mining library~\cite{demsar2013orange}. ABCN2e is an improvement of the original CN2 algorithm~\cite{clarkECML1991} for learning unordered rules. The differences between CN2 and ABCN2e are described in a technical report found at \url{https://ailab.si/abml.} General rule-learning algorithms, such as CN2, tend to generate large amounts of specific rules, which leads to more accurate results, however this makes them less appropriate for explaining. We will now describe a problem specific configuration of the rule-learning algorithm that extracts relevant and explainable patterns from student programs. |