A deep learning approach to detecting engagement of online learners

M. Ali Akber Dewan, Fuhua Lin, Dunwei Wen, Mahbub Murshed, Zia Uddin

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

43 Citations (Scopus)

Abstract

Online learning environments enable learning for the online learners. The motivational factors, like engagement, play an important role in effective learning. However, the learning designers did not take into consideration the motivational factors involved in the learning process. We believe that the next generation of online learning environments should have the functionality of tracking learner's engagement and thus provide personalized interventions. In this paper, we propose a deep learning-based approach to detecting online learners' engagement through using their facial expressions. Two-level (not-engaged and engaged) and three-level (not-engaged, normally-engaged and very-engaged) decisions are made on engagement detection during classification. We use Local Directional Pattern (LDP) to extract person-independent edge features for the different facial expressions and Kernel Principal Component Analysis (KPCA) to capture the nonlinear correlations among the extracted features. The experiment results show that the proposed method achieves a high accuracy in classification of different engagement levels that the learners may show during their online learning activities (e.g., reading, writing, watching video tutorials, and participating in online meetings). The experiments conducted on the Dataset for Affective States in E-Environments (DAiSEE) demonstrate the effectiveness of the proposed method, where the two-level engagement detection achieves a higher accuracy (90.89%) than the three-level engagement detection (87.25%).

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
EditorsFrederic Loulergue, Guojun Wang, Md Zakirul Alam Bhuiyan, Xiaoxing Ma, Peng Li, Manuel Roveri, Qi Han, Lei Chen
Pages1895-1902
Number of pages8
ISBN (Electronic)9781538693803
DOIs
Publication statusPublished - 4 Dec. 2018
Event4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018 - Guangzhou, China
Duration: 7 Oct. 201811 Oct. 2018

Publication series

NameProceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018

Conference

Conference4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Country/TerritoryChina
CityGuangzhou
Period7/10/1811/10/18

Keywords

  • Deep belief network
  • Engagement detection
  • KPCA
  • Local directional pattern
  • Online learning

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