Student Engagement from Video using Unsupervised Domain Adaptation

Chinchu Thomas, Seethamraju Purvaj, Dinesh Jayagopi

2022

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 task 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.

Paper Citation

in Harvard Style

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 - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 118-125. DOI: 10.5220/0010979400003209

in Bibtex Style

@conference{improve22,
author={Chinchu Thomas and Seethamraju Purvaj and Dinesh Jayagopi},
title={Student Engagement from Video using Unsupervised Domain Adaptation},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={118-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010979400003209},
isbn={978-989-758-563-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Student Engagement from Video using Unsupervised Domain Adaptation
SN - 978-989-758-563-0
AU - Thomas C.
AU - Purvaj S.
AU - Jayagopi D.
PY - 2022
SP - 118
EP - 125
DO - 10.5220/0010979400003209