Analyzing students' attention in class using wearable devices

Xin Zhang, Cheng Wei Wu, Philippe Fournier-Viger, Lan-Da Van, Yu-Chee Tseng

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

6 Scopus citations

Abstract

Detecting students' attention in class provides key information to teachers to capture and retain students' attention. Traditionally, such information is collected manually by human observers. Wearable devices, which have received a lot of attention recently, are rarely discussed in this field. In view of this, we propose a multimodal system which integrates a head-motion module, a pen-motion module, and a visual-focus module to accurately analyze students' attention levels in class. These modules collect information via cameras, accelerometers, and gyroscopes integrated in wearable devices to recognize students' behaviors. From these behaviors, attention levels are inferred for various time periods using a rule-based approach and a data-driven approach. The former infers a student's attention states using user-defined rules, while the latter relies on hidden relationships in the data. Extensive experimental results show that the proposed system has excellent performance and high accuracy. To the best of our knowledge, this is the first study on attention level inference in class using wearable devices. The outcome of this research has the potential of greatly increasing teaching and learning efficiency in class.

Original languageEnglish
Title of host publication18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017 - Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538627228
DOIs
StatePublished - 10 Jul 2017
Event18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017 - Macau, China
Duration: 12 Jun 201715 Jun 2017

Publication series

Name18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017 - Conference

Conference

Conference18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017
CountryChina
CityMacau
Period12/06/1715/06/17

Keywords

  • Activity Recognition
  • Attention Sensing
  • Body-Area Network
  • Machine Learning
  • Wearable Computing

Fingerprint Dive into the research topics of 'Analyzing students' attention in class using wearable devices'. Together they form a unique fingerprint.

Cite this