# Automated Code Extraction from Discussion Board Text Dataset

arXiv: https://arxiv.org/abs/2210.17495 · HTML: https://mlsanity.com/paper/2210.17495/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://mlsanity.com/paper/1605.02019/) (2016)

## Built on by (most cited first, 2 of 2)

- [A Computational Method for Measuring "Open Codes" in Qualitative Analysis](https://mlsanity.com/paper/2411.12142/) (2024)
- [Combining Automatic Coding and Instructor Input to Generate ENA Visualizations for Asynchronous Online Discussion](https://mlsanity.com/paper/2308.13549/) (2023)

## Abstract (arXiv)

This study introduces and investigates the capabilities of three different text mining approaches, namely Latent Semantic Analysis, Latent Dirichlet Analysis, and Clustering Word Vectors, for automating code extraction from a relatively small discussion board dataset. We compare the outputs of each algorithm with a previous dataset that was manually coded by two human raters. The results show that even with a relatively small dataset, automated approaches can be an asset to course instructors by extracting some of the discussion codes, which can be used in Epistemic Network Analysis.