The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Download
Citation
Articles | Volume XLVI-4/W5-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 445–449, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-445-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 445–449, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-445-2021

  23 Dec 2021

23 Dec 2021

TOWARDS WEBCAM-BASED FACE DIRECTION TRACKING TO DETECT LEARNERS' ATTENTION WITHIN ASYNCHRONOUS E-LEARNING ENVIRONMENT

N. T. A. Ramaha, İ. R. Karaş, E. Gül, M. R. Bozkurt, and R. Yayvan N. T. A. Ramaha et al.
  • Dept. of Computer Engineering, Karabuk University, Demir Celik Campus, 78050 Karabuk, Turkey

Keywords: COVID-19, E-Learning, Computer Vision, Face Direction, Asynchronous e-learning, Attention Detection

Abstract. Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners’ attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient.