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EduSense: Practical Classroom Sensing at Scale

hero image

EduSense represents the first real-time, in-the-wild evaluated and practically-deployable classroom sensing system at scale that produces a plethora of theoretically-motivated visual and audio features correlated with effective instruction.

Our getting started is a good starting point if you are interested in building/developing/deploying EduSense. More information about the team can be found on the EduSense website.

News

  • Oct 2019 We open-source our EduSense code!
  • Sep 2019 We presented our paper titled "Edusense: Practical Classroom Sensing at Scale" at Ubicomp'19.

Features for Students and Instructors

features

  • Visual Features:
    • Body Segmentation, Keypoints and Inter-frame tracking:
      • Hand Raise Detection
      • Upper Body Pose Estimation
      • Sit vs Stand Detection
      • Synthetic Accelerometer
      • Classroom Topology
    • Facial Lanndmarks and Attributes:
      • Smile Detection
      • Mouth State Detection
      • Gaze Estimation
  • Audio Features:
    • Speech Detection:
      • Student vs Instructor Speech
      • Speech Act Delimation
  • Classroom Digital Twins

Visualization Dashboard

viz dashboard

System Architecture

system architecture

Related Links

Citation

Karan Ahuja, Dohyun Kim, Franceska Xhakaj, Virag Varga, Anne Xie, Stanley Zhang, Jay Eric Townsend, Chris Harrison, Amy Ogan, and Yuvraj Agarwal. 2019. EduSense: Practical Classroom Sensing at Scale. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 71 (September 2019), 26 pages. DOI: https://doi.org/10.1145/3351229

@article{Ahuja:2019:EPC:3361560.3351229,
 author = {Ahuja, Karan and Kim, Dohyun and Xhakaj, Franceska and Varga, Virag and Xie, Anne and Zhang, Stanley and Townsend, Jay Eric and Harrison, Chris and Ogan, Amy and Agarwal, Yuvraj},
 title = {EduSense: Practical Classroom Sensing at Scale},
 journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
 issue_date = {September 2019},
 volume = {3},
 number = {3},
 month = sep,
 year = {2019},
 issn = {2474-9567},
 pages = {71:1--71:26},
 articleno = {71},
 numpages = {26},
 url = {http://doi.acm.org/10.1145/3351229},
 doi = {10.1145/3351229},
 acmid = {3351229},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Audio, Classroom, Computer Vision, Instructor, Machine Learning, Pedagogy, Sensing, Speech Detection, Teacher},
}

Karan Ahuja, Deval Shah, Sujeath Pareddy, Franceska Xhakaj, Amy Ogan, Yuvraj Agarwal, and Chris Harrison. 2021. Classroom Digital Twins with Instrumentation-Free Gaze Tracking. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 484, 1–9. DOI:https://doi.org/10.1145/3411764.3445711

@inproceedings{10.1145/3411764.3445711,
author = {Ahuja, Karan and Shah, Deval and Pareddy, Sujeath and Xhakaj, Franceska and Ogan, Amy and Agarwal, Yuvraj and Harrison, Chris},
title = {Classroom Digital Twins with Instrumentation-Free Gaze Tracking},
year = {2021},
isbn = {9781450380966},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3411764.3445711},
doi = {10.1145/3411764.3445711},
articleno = {484},
numpages = {9},
keywords = {digital twins., Classroom sensing, gaze tracking},
location = {Yokohama, Japan},
series = {CHI '21}
}

License

The source code in this directory and its subdirectories are all governed by BSD 3-Clause License unless otherwise noted in the source code. Once compiled or packaged, it is the user's reponsibility to ensure that any use of the result binary or image complies with any relevant licenses for all software packaged together.