Lightweight and Interpretable Detection of Affective Engagement for Online Learners

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

4 Citations (Scopus)

Abstract

Offering online learners with a personalized, ethical, and privacy-protecting pedagogical companion that can detect their affective engagement and that can run on the devices they use to learn (edge devices) requires not only generating lightweight models but also interpretable, trustworthy, and generalizable ones. SqueezeNet is a lightweight deep neural network architecture, which is more popular for the edge devices. This paper showcases how SqueezeNet appears not to provide as meaningful explanations of its predictions of academic emotions as those of a lightweight traditional CNN model. More specifically, the winning CNN model, consisting of 473,317 parameters, delivers a predictive accuracy of 99.4% on the testing set, while maintaining an identical level of descriptive accuracy. In contrast, the best SqueezeNet model comprises 141,301 parameters (3x less) and has a predictive accuracy of 93.7% and a dropping descriptive accuracy of 84.0%. In brief, the SqueezeNet model does not maintain a similarly high descriptive accuracy and does not effectively identify facial features corresponding to the confusion, distraction, enjoyment, neutrality, and fatigue emotions.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Pages176-184
Number of pages9
ISBN (Electronic)9781665421744
DOIs
Publication statusPublished - 2021
Event19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 - Virtual, Online, Canada
Duration: 25 Oct. 202128 Oct. 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

Conference

Conference19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Country/TerritoryCanada
CityVirtual, Online
Period25/10/2128/10/21

Keywords

  • affective computing
  • deep learning
  • edge devices
  • explainable artificial intelligence
  • facial expressions
  • online learning
  • student engagement

Fingerprint

Dive into the research topics of 'Lightweight and Interpretable Detection of Affective Engagement for Online Learners'. Together they form a unique fingerprint.

Cite this