ABSTRACT
COVID-19 has resulted in the increased use of distance learning around the world. With the advancement of information technology, traditional classroom teaching has gradually integrated the Internet and distance learning methods. Students need to be able to learn on their own in a distance learning environment, so their ability to self-regulate their learning in a distance learning environment cannot be ignored. However, in previous studies on self-regulated learning, most learners learn alone. When they have academic doubts, they cannot obtain help and support from their studies, resulting in reduced learning outcomes. This study uses the peer self-disciplined learning mechanism to establish a distance teaching system that assists students and to improve their own learning status by meeting with peers at a distance. It can also help learners orient themselves by observing their peers' learning status and goal considerations. The participants in this study were 112 college students in the department of information management. The control group used a general self-regulated teaching system for learning, and the experimental group used a distance learning system, incorporating peer self-regulated learning. The results of the study found that learners who used the distance peer learning mechanism were more effective than those who used the general distance self-regulated learning system;learners who used the distance peer-regulated learning mechanism had better motivation, self-efficacy, and reflection after the learning activity than those who used the general distance self-regulated learning system. In addition, with the aid of such mechanisms, learners' cognitive load can be reduced, and learning effectiveness can be improved. © 2023 by the authors.
ABSTRACT
SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation. © 2022 IEEE.