ML Anthology

Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)

Deliang Wang
AAAI 2024 pp. 23676-23678
doi:10.1609/AAAI.V38I21.30522 /aaai/2024/wang2024aaai-opening/

Abstract

This paper explores proposing interpreting methods from explainable artificial intelligence to address the interpretability issues in deep learning-based models for classroom dialogue. Specifically, we developed a Bert-based model to automatically detect student talk moves within classroom dialogues, utilizing the TalkMoves dataset. Subsequently, we proposed three generic interpreting methods, namely saliency, input*gradient, and integrated gradient, to explain the predictions of classroom dialogue models by computing input relevance (i.e., contribution). The experimental results show that the three interpreting methods can effectively unravel the classroom dialogue analysis, thereby potentially fostering teachers' trust.

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Cite

Text

Wang. "Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30522

Markdown

[Wang. "Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/wang2024aaai-opening/) doi:10.1609/AAAI.V38I21.30522

BibTeX

@inproceedings{wang2024aaai-opening,
  title     = {{Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)}},
  author    = {Wang, Deliang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23676-23678},
  doi       = {10.1609/AAAI.V38I21.30522},
  url       = {https://mlanthology.org/aaai/2024/wang2024aaai-opening/}
}