---
configs:
- config_name: v1_bea
  data_files: 
  - split: evaluation
    path: "data/v1_bea/evaluation_dataset-*.csv"
  - split: final_dataset
    path: "data/v1_bea/final_dataset-*.csv"
  - split: manual_evaluation
    path: "data/v1_bea/manual_evaluation_dialogues-*.csv"
- config_name: v2_sigcse
  data_files: 
  - split: train
    path: "data/v2_sigcse/train-*.csv"
  - split: evaluation
    path: "data/v2_sigcse/evaluation_dataset-*.csv"
  - split: final_dataset
    path: "data/v2_sigcse/final_dataset-*.csv"
  - split: manual_evaluation
    path: "data/v2_sigcse/manual_evaluation_dialogues-*.csv"
  - split: testset
    path: "data/v2_sigcse/testset-*.csv"
- config_name: v3
  data_files: 
  - split: final_dataset
    path: "data/v3/final_dataset-*.csv"
  - split: testset
    path: "data/v3/testset-*.csv"
---

# Socratic benchmark

## Dataset Description

The Socratic benchmark is a dataset of tutor-student interactions released by Erfan Al-Hossami et al. (see citations bellow). The dataset released here on HuggingFace for easier training and evaluation of feedback/tutor language models.  


## Dataset structure

The dataset structure matches the repository structure as released in the [GitHub repo](https://github.com/taisazero/socratic-debugging-benchmark). Each subset corresponds to a version, and each split a subrepo in the corresponding version folder.

## Languages

The assignments were written in Python. 


### Citation Information

All credits should be attributed to the original authors:

```
@inproceedings{al-hossami-etal-2023-socratic,
    title = "Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations",
    author = "Al-Hossami, Erfan  and
      Bunescu, Razvan  and
      Teehan, Ryan  and
      Powell, Laurel  and
      Mahajan, Khyati  and
      Dorodchi, Mohsen",
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.57",
    pages = "709--726",
    abstract = "Socratic questioning is a teaching strategy where the student is guided towards solving a problem on their own, instead of being given the solution directly. In this paper, we introduce a dataset of Socratic conversations where an instructor helps a novice programmer fix buggy solutions to simple computational problems. The dataset is then used for benchmarking the Socratic debugging abilities of GPT-based language models. While GPT-4 is observed to perform much better than GPT-3.5, its precision, and recall still fall short of human expert abilities, motivating further work in this area.",
}
```
```
@inproceedings{al-hossami-etal-2024-can,
author = {Al-Hossami, Erfan and Bunescu, Razvan and Smith, Justin and Teehan, Ryan},
title = {Can Language Models Employ the Socratic Method? Experiments with Code Debugging},
year = {2024},
isbn = {9798400704239},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3626252.3630799},
doi = {10.1145/3626252.3630799},
abstract = {When employing the Socratic method of teaching, instructors guide students toward solving a problem on their own rather than providing the solution directly. While this strategy can substantially improve learning outcomes, it is usually time-consuming and cognitively demanding. Automated Socratic conversational agents can augment human instruction and provide the necessary scale, however their development is hampered by the lack of suitable data for training and evaluation. In this paper, we introduce a manually created dataset of multi-turn Socratic advice that is aimed at helping a novice programmer fix buggy solutions to simple computational problems. The dataset is then used for benchmarking the Socratic debugging abilities of a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and chain of thought prompting of the much larger GPT-4. The code and datasets are made freely available for research at the link below.},
booktitle = {Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1},
pages = {53–59},
numpages = {7},
keywords = {benchmark dataset, debugging, language models, socratic dialogue},
location = {<conf-loc>, <city>Portland</city>, <state>OR</state>, <country>USA</country>, </conf-loc>},
series = {SIGCSE 2024}
}



### Contributions

[More Information Needed]