Educational Profiling of Guitar Tablature: Tools to Foster Self-Guided Learning

Digital education platforms attract learners with a wide range of ages and interests. Platforms are hence required to accommodate guidance accordingly. For music education, showing learners what it is about that phrase or transition that is challenging, being able to tell them which skills they have improved or are lacking, suggest ways of practising, are sought-after features. That level of nuance puts significant pressure on manual curation, calling for tools to assist or automate courseware production. To meet that need, this paper introduces a system to enrich annotation of guitar courseware. More concretely, a means to automatically profile guitar tablature, annotating it with educationally relevant tags and difficulty estimates, down to the note level. We validate the resulting descriptors on the task of predicting expert-assigned song syllabus levels. We report a 0.94 Mean Squared Error, on a dataset of 5000 songs, each categorised into one of ten levels. We also show how the descriptors can be used to visualise skill demands, and are interpretable as psychomotor constraints, opening the possibility to offer nuanced, personalised instruction and feedback.