  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.
