Adoption Social Media-Movie Based Learning Project (SMMBL) To Engage Students' Online Environment

Developing ICT as English Language Teaching (ELT) materials in Indonesia is still being studied in higher education contexts. Nowadays, amid covid 19 there are many tools, apps and technologies are being integration in virtual classroom. ICT can be used as media for teaching and learning process it should be related today’s fact by integrating ICT easy enough to share it and easy to access by the students’ technologies devices (Muthmainnah, et.all, 2021 and Apriani et.al, 2021 The purposes of this study is to investigate the impact of development instructional design incorporating social media-movie based learning project (SMMBLP) in Computer Science Faculty in learning English to engagement student’s online environment. The method of this study is quantitative research and used pre-experimental design to investigates the impact of SMMBLP on students’ English skill and student’s motivation in ELT. The participants in this study were the first semester students of Computer Science Faculty academic year 2020-2021 at Universitas Al Asyariah Mandar consisted 40 students. The data were collected by test;pre-test, post-test and questionnaire. The result of the study implied the impact on integration social media-movie based learning project (SMMBLP) that very effective to enhancing students’ English skill and motivation. The study suggests SMMBL can be integration in online or Hybrid/ Blended environment for the next education teaching model in ELT for EFL students. © 2022, Pegem Akademi Yayincilik Egitim Danismanlik Hizmetleri Ticaret A.S.. All rights reserved.

Detection of COVID-19 from radiology modalities and identification of prognosis patterns

SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.