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| author | Timotej Lazar <timotej.lazar@fri.uni-lj.si> | 2017-04-17 16:32:08 +0200 |
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| committer | Timotej Lazar <timotej.lazar@fri.uni-lj.si> | 2017-04-17 16:32:36 +0200 |
| commit | fa38d4ca99f863573b8900ef78d5191b8f2db561 (patch) | |
| tree | 2102c9f88b1c52e1fce2b2b62b3b7138de9dd195 /paper/introduction.tex | |
| parent | c3615b45b84e81e421763d76d01fa363dd79f985 (diff) | |
Fit the twelve (publisher remix)
Because the message should fit the medium.
Diffstat (limited to 'paper/introduction.tex')
| -rw-r--r-- | paper/introduction.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/paper/introduction.tex b/paper/introduction.tex index baf8ba0..f683fd0 100644 --- a/paper/introduction.tex +++ b/paper/introduction.tex @@ -7,7 +7,7 @@ Programming education is becoming increasingly accessible with massive online co 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}. +Developing a domain model typically requires significant knowledge-engineer\-ing 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 authoring 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). |