Adaptive structure metrics for automated feedback provision in Java programming

Today's learning supporting systems for programming mostly rely on pre-coded feedback provision, such that their applicability is restricted to modelled tasks. In this contribution, we investigate the suitability of machine learning techniques to automate this process by means of a presentationm of similar solution strategies from a set of stored examples. To this end we apply structure metric learning methods in local and global alignment which can be used to compare Java programs. We demonstrate that automatically adapted metrics better identify the underlying programming strategy as compared to their default counterparts in a benchmark example from programming.

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