| --- |
| license: cc |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - math |
| --- |
| |
| TLDR: MIT OCW Math Lectures with Student Questions |
|
|
| # SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts |
|
|
|
|
| <p align="center" style="font-size: 1.5em;"> |
| <a href="https://rosewang2008.github.io/sight/">Project Page</a> • |
| <a href="https://arxiv.org/pdf/2306.09343.pdf">Paper</a> • |
| <a href="https://github.com/rosewang2008/sight">Code</a> • |
| <a href="https://www.youtube.com/watch?v=Yt-2jLJLKjI">Video</a> |
| </p> |
| |
| Authors: Rose E. Wang*, Pawan Wirawarn*, Noah Goodman and Dorottya Demszky |
|
|
| *= Equal contributions |
| |
| In the Proceedings of Innovative Use of NLP for Building Educational Applications 2023 |
| |
| If you find our work useful or interesting, please consider citing it! |
| |
| ``` |
| @inproceedings{wang2023sight, |
| title={SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts}, |
| author={Wang, Rose E and Wirawarn, Pawan and Goodman, Noah and Demszky, Dorottya}, |
| year={2023}, |
| month = jun, |
| booktitle = {18th Workshop on Innovative Use of NLP for Building Educational Applications}, |
| month_numeric = {6} |
| } |
| ``` |
| |
|  |
| |
| ## Motivation |
| Lectures are a learning experience for both students and teachers. |
| Students learn from teachers about the subject material, while teachers learn from students about how to refine their instruction. |
| Unfortunately, online student feedback is unstructured and abundant, making it challenging for teachers to learn and improve. We take a step towards tackling this challenge. |
| First, we contribute a dataset for studying this problem: SIGHT is a large dataset of 288 math lecture transcripts and 15,784 comments collected from the Massachusetts Institute of Technology OpenCourseWare (MIT OCW) YouTube channel. |
| Second, we develop a rubric for categorizing feedback types using qualitative analysis. |
| Qualitative analysis methods are powerful in uncovering domain-specific insights, however they are costly to apply to large data sources. |
| To overcome this challenge, we propose a set of best practices for using large language models (LLMs) to cheaply classify the comments at scale. |
| We observe a striking correlation between the model's and humans' annotation: |
| Categories with consistent human annotations (>$0.9$ inter-rater reliability, IRR) also display higher human-model agreement (>$0.7$), while categories with less consistent human annotations ($0.7$-$0.8$ IRR) correspondingly demonstrate lower human-model agreement ($0.3$-$0.5$). |
| These techniques uncover useful student feedback from thousands of comments, costing around $\$0.002$ per comment. |
| We conclude by discussing exciting future directions on using online student feedback and improving automated annotation techniques for qualitative research. |
| |
| |
| ## Repository structure |
| |
| Scripts are in `run_analysis.sh` for replicating the paper analysis. Please refer to the `prompts` directory for replicating the annotations. |
| |
| The repo structure: |
| |
| ``` |
| . |
| ├── data |
| ├── annotations # Sample (human) and full SIGHT annotations |
| ├── comments # Per-video comments |
| ├── metadata # Per-video metadata like playlist ID or video name |
| └── transcripts # Per-video transcript, transcribed with Whisper Large V2 |
| ├── prompts # Prompts used for annotation |
| ├── results # Result plots used in paper |
| ├── scripts # Python scripts for analysis |
| ├── requirements.txt # Install requirements for running code |
| ├── run_analysis.sh # Complete analysis script |
| ├── LICENSE |
| └── README.md |
| ``` |
| |
| ## Installation |
| |
| To install the required libraries: |
| |
| ``` |
| conda create -n sight python=3 |
| conda activate sight |
| pip install -r requirements.txt |
| ``` |
| |
| ## Experiments |
| |
| TLDR: Running `source run_analysis.sh` replicates all the results we report in the paper. |
| |
| |
| Plots (e.g., the IRR comparison in Figure 3) are saved under `results/` as PDF files. |
| Numbers (e.g., sample data information in Table 2 or IRR values in Table 3) are printed out under `results/` as txt files. |
| |
| |
| ## Annotations |
| |
| The automated annotations provided in this GitHub repository have been scaled on categories with high inter-rater reliability (IRR) scores. |
| While we have made efforts to ensure the reliability of these annotations, it is important to note that the automated annotations may not be completely error-free. |
| We recommend using these annotations as a starting point and validating them through additional human annotation or other means as necessary. |
| By using these annotations, you acknowledge and accept the potential limitations and inherent uncertainties associated with automated annotation methods, like annotating at scale with GPT-3.5. |
| |
| We welcome any contributions to improve the quality of the annotations in this repository! |
| If you have made improvements to the annotations or expanded the annotations, feel free to submit a pulll request with your changes. |
| We appreciate all efforts to make these annotations more useful for the education and NLP community! |
