Playability Prediction in Digital Guitar Learning Using Interpretable Student and Song Representations

Digital music learning applications have become a popular option for self-guided learning of musical instruments. Personalization of the learning curriculum in such applications hinges on two essential components: the learning unit (song arrangement) and the learner (student). While previous research has focused extensively on quantifying and characterizing musical content, learner representation remains largely unexplored in digital music education. In this paper, we introduce interpretable representations for these components in the context of digital guitar learning. We propose a methodology to embed musical arrangements and individual guitar students into a shared, interpretable skill vector space. To achieve this, we employ an automated profiling technique for guitar tablatures, generating granular semantic descriptors and difficulty estimates. We validate the effectiveness of these representations by predicting the proportion of onsets played correctly by students, utilizing a large-scale dataset from an online guitar learning platform. Our results demonstrate that models leveraging the combined representation of students and song arrangements outperform informed baselines and show improved predictive accuracy compared to models using either representation individually. These findings underscore the value of joint learner–song arrangement representations for developing educational recommender systems that facilitate personalized learning of musical instruments.