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

arXiv: https://arxiv.org/abs/2407.12005 · HTML: https://mlsanity.com/paper/2407.12005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Reconstruction Network for Video Captioning](https://mlsanity.com/paper/1803.11438/) (2018)
- [Translating Videos to Natural Language Using Deep Recurrent Neural Networks](https://mlsanity.com/paper/1412.4729/) (2014)
- [Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation](https://mlsanity.com/paper/2306.05783/) (2023)
- [Hierarchical Modular Network for Video Captioning](https://mlsanity.com/paper/2111.12476/) (2021)
- [Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning](https://mlsanity.com/paper/1511.03476/) (2015)
- [Text with Knowledge Graph Augmented Transformer for Video Captioning](https://mlsanity.com/paper/2303.12423/) (2023)
- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning](https://mlsanity.com/paper/2211.15103/) (2022)
- [End-to-End Video Captioning](https://mlsanity.com/paper/1904.02628/) (2019)
- [Video Captioning: a comparative review of where we are and which could be the route](https://mlsanity.com/paper/2204.05976/) (2022)

## Abstract (arXiv)

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, textitVCEval, 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.