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Authors: Chinchu Thomas ; Seethamraju Purvaj and Dinesh Babu Jayagopi

Affiliation: Multimodal Perception Lab, International Institute of Information Technology Bangalore (IIIT-B), Karnataka, India

Keyword(s): Student Engagement, Unsupervised Domain Adaptation, Discrepancy and Adversarial Methods.

Abstract: Student engagement is the key to successful learning. Measuring student engagement is of utmost importance in the current global scenario where learning happens over online platforms. Automatic analysis of student engagement, in offline and online social interactions, is largely carried out using supervised machine learning techniques. Recent advances in deep learning have improved performance, albeit at the cost of collecting a large volume of labeled data, which can be tedious and expensive. Unsupervised domain adaptation using the deep learning technique is an emerging and promising direction in machine learning when labeled data is less or absent. Motivated by this, we pose our research question: ”Can deep unsupervised domain adaptation techniques be used to infer student engagement in classroom videos with unlabeled data?” In our work, two such classic techniques i.e. Joint Adaptation Network and adversarial domain adaptation using Wasserstein distance were explored for this tas k and posed as a binary classification problem along with different base models such as ResNet and I3D. The unsupervised domain adaptation results show significant improvement over the unsupervised baseline methods. (More)

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Paper citation in several formats:
Thomas, C.; Purvaj, S. and Jayagopi, D. (2022). Student Engagement from Video using Unsupervised Domain Adaptation. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-563-0; ISSN 2795-4943, SciTePress, pages 118-125. DOI: 10.5220/0010979400003209

@conference{improve22,
author={Chinchu Thomas. and Seethamraju Purvaj. and Dinesh Babu Jayagopi.},
title={Student Engagement from Video using Unsupervised Domain Adaptation},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE},
year={2022},
pages={118-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010979400003209},
isbn={978-989-758-563-0},
issn={2795-4943},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Student Engagement from Video using Unsupervised Domain Adaptation
SN - 978-989-758-563-0
IS - 2795-4943
AU - Thomas, C.
AU - Purvaj, S.
AU - Jayagopi, D.
PY - 2022
SP - 118
EP - 125
DO - 10.5220/0010979400003209
PB - SciTePress

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