ABSTRACT
Due to the COVID-19 outbreak in recent years, long distance learning has been prevalent. The increasing need of online learning calls for the effectiveness of the learning evaluation method in digital learning through human-computer interaction (HCI) to be strengthened. Therefore, this study collects bigdata images through computer vision, collects data on the students' emotional and behavioral dimensions under multidimensionality, and analyzes these data through deep learning (DL) with the student engagement model. This proposes an online student learning engagement analysis system based on biometrics image processing applied to artificial intelligence (AI). In the prediction of student participation, emotional and behavioral dimension data obtained from different students will be used to find out the weight relations between these two engagement dimensions and engagement value through the regression model of DL and evaluate participants' engagement status in online learning. © 2022 IEEE.
ABSTRACT
Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future. © 2020 Authors.