ConstrucTED : Constructing Tailored Educational Datasets From Online Courses: Constructing Tailored Educational Datasets From Online Courses
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2 mai 2024
- handle: 10670/1.ob0l69
- hal: hal-04510615
- doi: 10.5220/0012745000003693
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info:eu-repo/semantics/altIdentifier/doi/10.5220/0012745000003693
http://creativecommons.org/licenses/by-nc-nd/ , info:eu-repo/semantics/OpenAccess
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Educational Resources DatasetsSujets proches
Hours (Time) Natural science Sciences Science of science Learning process Biblical cosmogony Scholarship LearningCiter ce document
Aymen Bazouzi et al., « ConstrucTED : Constructing Tailored Educational Datasets From Online Courses: Constructing Tailored Educational Datasets From Online Courses », HALSHS : archive ouverte en Sciences de l’Homme et de la Société, ID : 10.5220/0012745000003693
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Researchers are actively involved in developing various systems to support education, including recommender systems. However, to create and evaluate such systems, they require rich and versatile datasets about educational content. At times, the available data proves insufficient, leading researchers to invest significant time in crafting personalized web scrapers for additional data retrieval. The generated datasets are often task-specific and may be time-consuming to adapt to future tasks. Additionally, researchers may encounter licensing issues when using courses from different providers. Furthermore, researchers prefer evaluating their methods through diverse tests, involving datasets with varying characteristics. However, this diversity is not commonly found in most available datasets, at least not explicitly so. To address these challenges, we introduce ConstrucTED, a tool built on top of Google APIs, enabling the efficient creation of custom educational datasets from YouTube playlists. This allows datasets to be tailored to specific characteristics such as a predetermined number of courses, coverage of specific topics, or courses from a particular university. ConstrucTED creates datasets from video course transcripts, providing a ready-to-use solution that significantly shortens the time required to create such datasets. The resulting datasets are versatile and suitable for tasks like classification and learning path creation.