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arXiv · 2407.12005

VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It

Xiaoxuan Zhu·Zhouhong Gu·Sihang Jiang·Zhixu Li·Hongwei Feng·Yanghua Xiao
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Abstract

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.

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Xiaoxuan Zhu, Zhouhong Gu, Sihang Jiang, Zhixu Li, Hongwei Feng, Yanghua Xiao. 2024-06-15. VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It. https://arxiv.org/abs/2407.12005

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