Create README.md

README.md

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+---
+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
+
+[Project Page](https://rosewang2008.github.io/sight/), [Paper](https://arxiv.org/pdf/2306.09343.pdf), [Poster](assets/poster.pdf)
+
+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}
+}
+```
+
+![Main Figure](assets/fig1.png)
+
+## 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!