Akande, Oluwatobi Noah and Badmus, Taofeeq Alabi and Akindele, Akinyinka Tosin and Arulogun, Oladiran Tayo (2020) Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement. Elsevier, 31 (105926). pp. 1-7.
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Abstract
The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face-to-face University students during the COVID-19 pandemic lockdown. An online Google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and the reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face-to-face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face-to-face University students are with these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | AKINWUMI |
| Date Deposited: | 19 Jun 2023 13:19 |
| Last Modified: | 19 Jun 2023 13:19 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/17048 |
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