Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement

Oluwatobi, Akande Noah; Badmus, Taofeeq Alabi; Akindele, Akinyinka Tosin; Arulogun, Oladiran Tayo

doi:10.1016/j.dib.2020.105926

Cited by 19 publications

(20 citation statements)

References 2 publications

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“…This indicates that the quarantine has indeed contributed to an increase in the academic rate. This is consistent with that confirmed by the study of Akande, et al, (2020) and Al awamrah, (2015). The authors state that studying online in a university environment is a challenge to the cost of equipment (hardware and software).…”

Section: Discussionsupporting

confidence: 93%

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Student Perceptions of Ecuadorian Virtual Platforms During the Covid-19 Pandemic

Velazco

Martínez

Cejas

et al. 2021

PEC

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In February 2020 Ecuador declared a health emergency due to the Covid-19 pandemic. On-site classes were suspended. The Ecuadorian university population does not adapt to virtual educational platforms. The objective of the study was to analyse student perception of Ecuadorian educational platforms in higher education during the COVID-19 pandemic. The study was quantitative. The sample was non-probabilistic participatory, consisting of students from different universities in the Province of Chimborazo. For data collection, a questionnaire with four study variables was applied. The results were analysed with T student and MANOVA. There is a positive perception of students using the educational platforms. There were significant statistical differences according to gender variables, contrary to the levels of study and location, there is a greater sympathetic affection of students when receiving virtual classes by female teachers. It can be concluded that the Covid-19 pandemic accelerated the adoption of online learning by higher education institutions. Keywords: Covid-19, virtual education, higher education, Ecuador, education platform, virtual education, Ecuador

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Section: Discussionsupporting

confidence: 93%

“…It provides interactivity to remote participants through open access channels (Internet, etc.) (Akande, et al, 2020).…”

Section: Distance Learningmentioning

confidence: 99%

Student Perceptions of Ecuadorian Virtual Platforms During the Covid-19 Pandemic

Velazco

Martínez

Cejas

et al. 2021

PEC

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“…It was observed that 97.3% of the respondents use smartphones to access the internet while 53.7% use laptops. This conforms with existing surveys [2] , [3] and this was attributed to the fact that smartphones guarantee ubiquitous connectivity to the internet. The top 10 social media platforms used by the respondents are illustrated in Fig.…”

Section: Data Descriptionsupporting

confidence: 91%

Dataset of Moroccan nursing students’ intention to use and accept information and communication technologies and social media platforms for learning

et al. 2021

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The outbreak of COVID-19 has redefined how we carry out our daily activities with emphasis on safety measures like social distancing, the use of face masks and proper hygiene. With nursing personnel at the forefront of combating the menace created by this pandemic, the use of ICT has remained a vital technology that must be embraced by all in order to carry out a safe nursing practice. Therefore, becoming ICT compliant is no more an option to stay relevant and alive during this COVID era. This data article presents the outcome of a survey carried out among Moroccan nursing students. This was done with a view to assess their readiness and willingness to accept and use ICT as well as social media for learning and discharging their duties during and after this COVID era. Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted to structure the questionnaire items used in this survey. This was then administered to Moroccan nursing students via online Google forms. Seven hundred and two (702) respondents completed the questionnaire. After data screening and cleaning, normality assessment of the data was carried out in terms of skewness and kurtosis. Afterwards, Partial Least Square Structural Equation Modeling (PLS-SEM) was employed to establish the statistical significance of the data in terms of the reflective indicator loadings, internal consistency reliability and convergence. This dataset includes the questionnaire used (in English and French), the responses obtained in spreadsheet format, the charts generated from the responses received, the SPSS file and the statistical analysis file. This dataset will help policy makers understand how nursing students use ICT and social media platforms and how these could be adopted as a more secure means of learning and discharging their during the COVID-19 era and beyond.

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“…We are living in an exceptionally globalized world, where relocation adaptability, free travel among nations, and the turn of events and utilization of Information and Communication Technologies (ICTs) are essentially developed [ 7 ]. We should feature the developing interconnection between the world’s economies, which is reflected in the financial progress and individual knowledge [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Chintalapudi

Battineni

Amenta

2021

Infectious Disease Reports

131

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The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88–87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

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Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement

Cited by 19 publications

References 2 publications

Student Perceptions of Ecuadorian Virtual Platforms During the Covid-19 Pandemic

Student Perceptions of Ecuadorian Virtual Platforms During the Covid-19 Pandemic

Dataset of Moroccan nursing students’ intention to use and accept information and communication technologies and social media platforms for learning

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

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