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.

翻译：本文描述了在计算机科学专业高等教育学生的课后作业中采用众包技术所遵循的方法及经验教训。通过使用支持文化遗产领域众包任务的平台，学生被要求丰富特定音乐曲目选择的元数据。后续采用语义网技术对学生参与活动的成果进行了进一步分析与利用。共有98名学生参与了此次活动，贡献了超过6400条涉及854首曲目的标注数据。该过程还生成了一个公开可用的标注数据集，该数据集可用于音乐标签任务的机器学习模型。通过活动结果及在线调查收集的反馈，我们总结出将众包融入计算机科学课程的优势与挑战，以及这一方式如何提升学生学习参与度的相关见解。