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Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback

Authors: Maximilian Jahnke; Frank Höppner;

Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback

Abstract

The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java instructions with reality. The students' submissions are analyzed for repeating mistakes across different exercises by representing the context surrounding the error by a graph and applying graph mining techniques to discover their common grounds. The patterns need to be annotated only once and help to address misconceptions of individual students. They may also be used to select follow-up exercises automatically, that contain the same intricacy.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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