--- license: cc-by-sa-4.0 tags: - Engagement --- # CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels ## Project page is [here](https://jasonwuchi.github.io/CMOSE/) ## Video clip name Each video clip is named as videoX_Y_personZ, which means it is the Yth clip of the Zth subject from coaching session X. ## Openface We extract the second level features from [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation). The extracted files are stored under "secondfeature/videoX_Y_personZ.csv". These features include: - **Gaze Direction and Angles** - Three coordinates to describe the gaze direction of left and right eyes respectively - Two scalars to describe the horizontal and vertical gaze angles - **Head Position** - Three coordinates to describe the location of the head relative to the camera - **Head Rotation** - Rotation of the head described with pitch, yaw, and roll - **Facial Action Units (AUs)** - Intensities of 17 AUs represented as scalars - Presence of 18 AUs represented as scalars ## I3D We use the [I3D Repository](https://github.com/v-iashin/video_features) to extract the I3D vectors. One I3D vector is extracted for each clip. The features are stored in "final_data_1.json". ## Acoustics We use [ParselMouth](https://github.com/YannickJadoul/Parselmouth) to extract the acoustics features. They are stored in "label_results_w_audio_final.json". We also calculate the high level features such as the percentage of high/low volume, high/low pitch, and std of volume/pitch. These are stored in "new_bert_ac_dict.json". ## Narrations We collect the narrations from the Live Transcript functions in Zoom. They are stored in "label_results_w_audio_final.json". We also extract the bert features from the narrations and store them in "new_bert_ac_dict.json". ## Data split Split information can be found in "final_data_1.json". Note that "split" should be one of "train", "unlabel", and "test". We use "unlabel" for validation purposes.