Sensor-based Methodological Observations for Studying Online Learning


Studying Online learnersOnline learning has gained increased popularity in recent years. However, with online learning, teacher observation and inter- vention is lost, creating a need for technologically observable characteristics that can compensate for this limitation. The present study used a wide array of sensing mechanisms including eye tracking, galvanic skin response (GSR) recording, facial expression analysis, and summary note-taking to monitor participants while they watched and recalled an online video lecture. We explored the link between these human- elicited responses and learning outcomes as measured by quiz questions. Results revealed GSR to be the best indicator of the challenge level of the lecture material. Yet, eye tracking and GSR remain difficult to capture when monitoring online learn- ing as the requirement to remain still impacts natural behavior and leads to more stoic and unexpressive faces. Continued work on methods ensuring naturalistic capture are critical for broadening the use of sensor technology in online learning, as are ways to fuse these data with other input, such as structured and unstructured data from peer-to-peer or student-teacher interactions.
Collaborators
  • Ashley Edwards, SUNY Geneseo
  • Anthony Massicci, Onondaga Community College
  • Srinivas Shridharan, Stevens Institute of Technology
  • Cecilia Ovesdotter Alm, Department of English, Rochester Institute of Technology
  • Reynold Bailey, Department of Computer Science, Rochester Institute of Technology
  • Linwei Wang, Golisano College of Computing and Information Sciences, Rochester Institute of Technology
  • Publications
  • Edwards, A. A., A. Massicci, S. Sridharan, J. Geigel, L. Wang, R. Bailey, and C.O. Alm (2017). Sensor-based Methodological Observations for Studying Online Learning. In: Proceedings of the 2017 ACM Workshop on Intelligent Interfaces for Ubiquitous and Smart Learning. SmartLearn 17. Limassol, Cyprus: ACM, pp.2530.