The Covid-19 pandemic brought changes in people's lives and in various sectors of the economy, including the education sector. In this context, students and teachers were able, through digital technologies, to continue their academic activities even in these difficult times. Many of these digital technologies were used to bridge the gap between instructors and students. Thus, in this work we reviewed the literature to better understand the usage of one of the widely known tools adopted by several schools: the Google Classroom (GC). For a more conclusive analysis, we used a systematic literature review to obtain relevant articles (N=25), and then, we grouped them by similarity using clustering algorithms to facilitate data analysis and summarization. The best clustering algorithm among the three compared was the k-medoids, as it generated clusters with better data separation. In our research, 56% of papers were published in 2020 and 44% of papers were published in 2021. Most of the articles reported positive reviews (72%) of using Google Classroom during the pandemic due to its good usability and the possibility of browsing through hyperlinks. Nevertheless, some weaknesses were found in 28% of papers (negative or neutral review) when teachers or students faced instability in the network. In addition, some authors have presented solutions to get around the problem about the GC usage during pandemic, such as the use of other networks to support learning and the use of tools that allow synchronous communication between teacher and student.
Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patterns. This disorder has no cure and has different levels of severity that vary according to the appearance of symptoms in each patient. Generally, the waiting time for the diagnosis of autism spectrum disorder is slow, having as one of the reasons for this situation the lack of development of simple screening procedures to be implemented and which have efficient results. The objective of this work is to analyze a public database in order to find patterns of the autism spectrum. That is, to isolate the attributes that together with behavioral characteristics can bring greater reliability to the precursor model. The preliminary results showed that the probabilistic neural network algorithm performed well in this classification. In addition, the application of correlation filters demonstrated greater efficiency in accuracy. By applying 8 data mining algorithms and aggregating the demographic, individual and behavioral attributes, and excluding some attributes, we obtained an accuracy of 100% through the Support Vector Machine. Finally, the results with machine learning have shown that the patient’s ethnicity, continent and the presence of jaundice, tend to reveal more likely that the patient will be diagnosed with autism spectrum disorder.
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