  In this paper, we introduce score difficulty classification as a sub-task of
music information retrieval (MIR), which may be used in music education
technologies, for personalised curriculum generation, and score retrieval. We
introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147
piano pieces in symbolic representation and the corresponding difficulty labels
derived by its composer B\'ela Bart\'ok and the publishers. As part of our
methodology, we propose piano technique feature representations based on
different piano fingering algorithms. We use these features as input for two
classifiers: a Gated Recurrent Unit neural network (GRU) with attention
mechanism and gradient-boosted trees trained on score segments. We show that
for our dataset fingering based features perform better than a simple baseline
considering solely the notes in the score. Furthermore, the GRU with attention
mechanism classifier surpasses the gradient-boosted trees. Our proposed models
are interpretable and are capable of generating difficulty feedback both
locally, on short term segments, and globally, for whole pieces. Code,
datasets, models, and an online demo are made available for reproducibility
