VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913635468574720 |
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| author | Zhu, Xiaoxuan Gu, Zhouhong Jiang, Sihang Li, Zhixu Feng, Hongwei Xiao, Yanghua |
| author_facet | Zhu, Xiaoxuan Gu, Zhouhong Jiang, Sihang Li, Zhixu Feng, Hongwei Xiao, Yanghua |
| contents | Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges. In this work, we focus on the task of automatically evaluating the quality of video course content. We have constructed a dataset with a substantial collection of video courses and teaching materials. We propose three evaluation principles and design a new evaluation framework, \textit{VCEval}, based on these principles. The task is modeled as a multiple-choice question-answering task, with a language model serving as the evaluator. Our method effectively distinguishes video courses of different content quality and produces a range of interpretable results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It Zhu, Xiaoxuan Gu, Zhouhong Jiang, Sihang Li, Zhixu Feng, Hongwei Xiao, Yanghua Multimedia Computer Vision and Pattern Recognition Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges. In this work, we focus on the task of automatically evaluating the quality of video course content. We have constructed a dataset with a substantial collection of video courses and teaching materials. We propose three evaluation principles and design a new evaluation framework, \textit{VCEval}, based on these principles. The task is modeled as a multiple-choice question-answering task, with a language model serving as the evaluator. Our method effectively distinguishes video courses of different content quality and produces a range of interpretable results. |
| title | VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.12005 |