@article{morris2013using,
author = {Morris, Dan and Fiebrink, Rebecca},
title = {Using Machine Learning to Support Pedagogy in the Arts (2013)},
year = {2013},
month = {December},
abstract = {Teaching artistic skills to children presents a unique challenge: High-level creative and social elements of an artistic discipline are often the most engaging and the most likely to sustain student enthusiasm, but these skills rely on low-level sensorimotor capabilities, and in some cases rote knowledge, which are often tedious to develop. We hypothesize that computer-based learning can play a critical role in connecting ‘‘bottom-up’’ (sensorimotor-first) learning in the arts to ‘‘top-down’’ (creativity-first) learning, by employing machine learning and artificial intelligence techniques that can play the role of the sensorimotor expert. This approach allows learners to experience components of higher-level creativity and social interaction even before developing the prerequisite sensorimotor skills or academic knowledge.},
publisher = {Springer},
url = {http://approjects.co.za/?big=en-us/research/publication/using-machine-learning-support-pedagogy-arts/},
pages = {1631-1635},
journal = {Personal and Ubiquitous Computing},
volume = {17},
}