The paper proposes MOSAIC-F, a multimodal feedback pipeline for oral presentations that combines human rubrics, sensors, and large language models, but it contains no outcome data or evaluation.
Co-Attention Based Neural Network for Source-Dependent Essay Scoring
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This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.
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MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
The paper proposes MOSAIC-F, a multimodal feedback pipeline for oral presentations that combines human rubrics, sensors, and large language models, but it contains no outcome data or evaluation.