scholarly journals A ‘DIGITAL PEDAGOGIES’–BASED LEARNING PLATFORM

Author(s):  
Matthew Montebello ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 210-215
Author(s):  
Flavia Kaba

Abstract Due to the rapid developments in educational technology, today’s professors are in search of exploring innovative techniques in order to promote involvement of students in the learning process in general and in the foreign language learning process in particular. This is why today’s students are seen as digital-natives and being motivated for the learning process is very difficult if the modern technology they are familiar with is not utilized effectively in the classroom. When it comes to the assessment part of this process, the situation may become worse, as most of the students feel unwilling due to anxiety problems in general and foreign language anxiety in particular. This study presents an innovative way of assessing students’ skills that they gain during foreign language learning process introducing Edmodo, which is an educational social network that provides a secure learning platform for students and educators. This study is a descriptive one, based on the analyses, surveys, and opinions of different researchers that have implemented this platform in their teaching process. The main objective is to introduce the implementation of various assessment applications through Edmodo.


2016 ◽  
Vol 15 (5) ◽  
pp. 109-130 ◽  
Author(s):  
Mohsen El-Shawarby
Keyword(s):  

Author(s):  
Sini Jokiniemi ◽  
Jussi Myllärniemi ◽  
Timo Poranen ◽  
Marika Vuorenmaa
Keyword(s):  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.


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