Video visualization profile analysis in online courses
In this article, student video visualization profiles are analyzed with two objectives: 1) to identify difficult sections in videos and 2) to predict student performance based on their video visualization profiles. For identifying critical sections in videos two novel indicators are proposed. The fi...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/713086 |
| Acceso en línea: | http://hdl.handle.net/10486/713086 https://dx.doi.org/10.1109/TE.2024.3396296 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine Learning Massive Open Online Course (MOOC) Online Learning Video Analytics Informática |
| Sumario: | In this article, student video visualization profiles are analyzed with two objectives: 1) to identify difficult sections in videos and 2) to predict student performance based on their video visualization profiles. For identifying critical sections in videos two novel indicators are proposed. The first one is designed to measure the complexity of the concept being described. The second proposal, identifies video sections that are more visually complex. For the first indicator, the average number of forward and backward passes are used. The higher the number of backward (forward) passes over a region, the more challenging (easy) the section is. For identifying sections with complex visuals, the number of pauses is recorded. Finally, the student performance prediction is carried out with the purpose of detecting the alignment between videos and their related questions. The results show that video visualization profiles are a good tool to identify video and question alignment |
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