Blind Special Education Students' Mathematics Psychology in the Pandemic: The Impact of Flipped Classroom Learning

Blind students' mathematics learning results during the Covid-19 epidemic will be examined in this research using a Flipped Classroom approach. This research makes use of a quasi-experimental approach that includes non-equivalent control groups. Blind kids in grades IV and V make up the study's population of 16 participants. Saturated sampling was used to collect the samples. Students in the experimental class are taught using the Flipped Classroom paradigm, while those in the control class are taught using a direct learning approach as a comparison. A p-value greater than 0.05 indicates that the data are normal and homogenous. The average of two unpaired samples from data is compared using the independent sample t-test. There is no H0 for a t-statistic of less than 0.05;if it is less than 0.05, it is not accepted. First, the Sig.t count (2-tailed) or the P-value are compared to make the first decision. After the second judgment, H0 is no longer a valid hypothesis. Learning arithmetic in a traditional classroom or without any treatment utilizing the Flipped Classroom methodology was shown to be less beneficial for blind pupils during the covid-19 outbreak © 2023, Journal for ReAttach Therapy and Developmental Diversities.All Rights Reserved.

The Interaction of Teachers in Online Learning During the Pandemic (A Study Comparison of Teacher and Student Perspectives)

Online learning is the solution chosen to avoid crowds, which are feared to lead to the new cluster of the Covid-19 pandemic. It's just that online learning is often followed by a different process of teacher interaction in online learning.This study is intended to describe teacher interactions in online learning during the pandemic between teacher and student perspectives.The research approach is quantitative with the type of comparative research, through random sampling, namely on respondents who are in teacher and student social media forums, from January to March in 2021.The results of data collection obtained 789 teachers and 910 student respondents. The data collection instrument used a closed questionnaire.The results showed interaction of teachers providing online learning during the pandemic at all levels of education is significantly different andthere is a significant difference between a teacher and student learning interactions, at the level of achievement of teacher and student respondents at certain times/times both have had online learning interactions during the pandemic. For teachers the average value is 2.55 with a percentage of 56.1%, for students, the average value is 2.55 with a percentage of 63.9%. The researcher recommends that it is necessary to reflect and evaluate teacher interactions in online learning. Copyright © 2022, Anka Publishers. All rights reserved.

Mathematics Performance Among Pre-Diploma Students with Online Learning Approach During Movement Control Order (MCO)

Various teaching and learning approaches have been applied among educators to the students during the Movement and Control Orders (MCO) employed due to COVID-19. Educators especially those who are teaching the mathematics subject require comprehensive and effective tools to help their students understand the concept and able to do exercises with less face-to-face guidance or a normal conventional teaching approach. Teaching the mathematics subject especially to the Pre-Diploma students is very challenging as they are quite weak in mathematics fundamentals and coincidentally, they have to face the obstacles of teaching approach with Online learning during MCO. Hence, a mathematics teaching model with an online learning approach was created to make the teaching delivery effective, thus increasing the learning curve or performance of mathematics among students. The ongoing and final assessment results of the students were analysed using an independent sample t-test to measure the difference between those experiencing and without experiencing the suggested model.

Comparison of Convolutional Neural Network Architectures for Face Mask Detection

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A cost-effective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely;AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.

Movement Control Order (MCO): An Syar’iyyah Political Approach

Covid-19 is a pandemic that has been declared by the World Health Organization (WHO) as a pandemic that has claimed many lives around the world. As of January 1, 2021, the world has recorded at least two million infected victims. Implementing the movement control order is one of the best ways implemented by the government today in curbing the Covid-19 epidemic until a vaccine is available. The method of discussion found in this article is based on document analysis by referring to authoritative books, journals, articles, and websites. The study’s findings found that the Movement Control Order implemented by the Malaysian government is based on ‘Siyasah Syar’iyyah’ in maintaining and curbing the spread of Covid-19 in Malaysia. © 2021. RIGEO • 11(7), SPRING. All Rights Reserved.