Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education
Abstract
This paper describes the methodology followed and the lessons learned from employing crowdsourcing techniques as part of a homework assignment involving higher education students of computer science. Making use of a platform that supports crowdsourcing in the cultural heritage domain students were solicited to enrich the metadata associated with a selection of music tracks. The results of the campaign were further analyzed and exploited by students through the use of semantic web technologies. In total, 98 students participated in the campaign, contributing more than 6400 annotations concerning 854 tracks. The process also led to the creation of an openly available annotated dataset, which can be useful for machine learning models for music tagging. The campaign's results and the comments gathered through an online survey enable us to draw some useful insights about the benefits and challenges of integrating crowdsourcing into computer science curricula and how this can enhance students' engagement in the learning process.
Keywords
Cite
@article{arxiv.2306.07310,
title = {Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education},
author = {Vassilis Lyberatos and Spyridon Kantarelis and Eirini Kaldeli and Spyros Bekiaris and Panagiotis Tzortzis and Orfeas Menis - Mastromichalakis and Giorgos Stamou},
journal= {arXiv preprint arXiv:2306.07310},
year = {2023}
}
Comments
Presented in The 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), Berlin, Germany, 31 June-2 July 2023. For The GitHub code for the created music dataset, see https://github.com/vaslyb/MusicCrowd