The impact of a usefulness intervention on students’ learning achievement in a virtual biology lesson: An eye-tracking-based approach

Ferdinand, Joseph; Gao, Hong; Stark, Philipp; Bozkir, Efe; Hahn, Jens-Uwe; Kasneci, Enkelejda; Göllner, Richard

doi:10.1016/j.learninstruc.2023.101867

Cited by 3 publications

(1 citation statement)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, most research on cognitive engagement in learning adopts a combination of subjective self-reporting and cognitive testing for analysis. Some studies used physiological indicators for analysis and evaluation [6] and some used computer vision to detect learners' emotions and behavioral performance for automated analysis, etc. In recent years, researchers have been developing systems that can monitor learners' attention, emotion, cognitive load, and other cognitive states in real time, to provide personalized learning support and assessment.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Analysis System for Teaching and Learning Cognitive Engagement Based on Computer Vision in an Immersive Virtual Reality Environment

Li,

Wang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education.

show abstract

Section: Introductionmentioning

confidence: 99%