Graphic Model of Virtual Teaching Supervision through Fuzzy Logic in Non-University Educational Centers.
Nuria Falla-FalcónEloy López-MenesesMiguel-Baldomero Ramírez-FernándezEsteban Vázquez-CanoPublished in: International journal of environmental research and public health (2022)
This research analyzes the supervision of non-university virtual training due to the unexpected non-face-to-face teaching scenario caused by COVID-19 with a graphic model using the SULODITOOL ® instrument. It arises as a research line of the Chair of Education and Emerging Technologies, Gamification and Artificial Intelligence of the Pablo de Olavide University (Seville) and is developed under the auspices of other assessment instruments within the framework of the functions and attributions of the Education Inspectorate of Spain. The aforementioned instrument is made up of 10 weighted supervisory indicators using fuzzy logic. The aggregation of linguistic variables of 242 expert judges was performed using the probabilistic OR function and defuzzified using the area centroid method to calculate the aforementioned weights. Based on the innovative analytical and graphic methodology used to analyze the supervision of virtual teaching, both synchronous and asynchronous, it stands out from the results obtained that there are certain supervision indicators, such as the training design and the methodology used, which should be considered as factors key in all the scenarios studied (primary education, compulsory secondary education and post-compulsory education).
Keyphrases
- healthcare
- artificial intelligence
- quality improvement
- machine learning
- medical students
- coronavirus disease
- magnetic resonance
- deep learning
- big data
- climate change
- patient reported outcomes
- magnetic resonance imaging
- clinical practice
- mass spectrometry
- medical education
- network analysis
- respiratory syndrome coronavirus