@inproceedings{f7c9d8dc67b645649a76fb9f7c4e5591,
title = "Assessing a music student's progress",
abstract = "Teachers frequently make errors when assessing music students. We propose a machine learning application that, given two performances of a piece of music, determines which performance is better, providing an objective and accurate assessment of progress. Several features are extracted from performances using music analysis algorithms, creating a vector of features for each performance. The vectors from two performances of a piece of music are subtracted from each other, and this vector of differences is input to a machine learning classifier which maps the vector to an assessment of progress. The implementation demonstrates that such a tool is feasible.",
keywords = "Assessment, Learning analytics, Machine learning, Music education",
author = "Joel Burrows and Vivekanandan Kumar and Kinshuk and Ali Dewan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018 ; Conference date: 09-07-2018 Through 13-07-2018",
year = "2018",
month = aug,
day = "10",
doi = "10.1109/ICALT.2018.00055",
language = "English",
isbn = "9781538660492",
series = "Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018",
pages = "202--206",
editor = "Nian-Shing Chen and Maiga Chang and Ronghuai Huang and K. Kinshuk and Kannan Moudgalya and Sahana Murthy and Sampson, {Demetrios G}",
booktitle = "Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018",
}