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。借助一个支持文化遗产领域众包活动的平台，学生被要求对所选音乐曲目的相关元数据进行丰富。随后，学生通过语义网技术对众包活动的结果进行进一步分析与利用。共计98名学生参与了该活动，贡献了超过6400条标注，涉及854首曲目。该过程还生成了一份公开可用的标注数据集，对用于音乐标签的机器学习模型具有实用价值。通过活动结果及在线问卷调查收集的反馈，我们得以得出关于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有益启示。