Post COVID-19: New breakthroughs and the future of behavioural research data collection




Post COVID-19, research, breakthrough, Data collection, connectivism learning theory


Behavioural researchers have been faced with challenges associated with the choice of data collection methods that is timely and cost-effective for all situations. Several studies have examined various means of collecting data while some electronic means of data collection have been explored. However, there is a need for a study that compares the conventional and contemporary data collection methods in terms of profile, perceptions and prospects. Therefore, this study examined the new breakthroughs and the future of behavioural research data collection in post COVID-19 era. The study is underpinned by connectivism learning theory within ex-post facto design with a sample of one hundred and twenty-six (126) behavioural science researchers. Post COVID-19 Data Collection Methods Scale-Forms App (r=0.86) was used, and the data collected were analysed using frequency count and t-test. The findings showed that there were more users of breakthrough methods 47 (37.3%) than conventional 39 (30.9%) and mixed method 40 (31.7%). Conventional methods were less available than new breakthrough methods. There is a significant difference in the perception, challenges and prospects of the conventional and breakthroughs in behavioural research data collection methods, all in favour of new breakthroughs. It is, therefore, recommended that behavioural researchers, as well as other researchers, avail themselves of the opportunities offered by the new breakthrough to advance their research endeavours in post COVID-19 era.


Esteban Jr, A. M., & Cruz, M. J. P. (2021). Digital divide in times of pandemic among teacher education students. Open Access Library Journal, 8(4), 1-12.

Alimin, E. (2020). Breakthrough Method. Indonesia.

Anderson, C., Thea, and Renieris, E. M. (2020). Data protection and digital infrastructure before, during and after a pandemic. Omidyar Network.

Apuke, O. D. (2017). Quantitative research methods: A synopsis approach. Kuwait Chapter of Arabian Journal of Business and Management Review, 33(5471), 1-8.

Bayyan Sr, A. F. (2016). One-to-one mobile technology and standardized testing: A quantitative ex post facto study (Doctoral dissertation, University of Phoenix).

Bell, F. (2011). Connectivism: Its place in theory-informed research and innovation in technology-enabled learning. International Review of Research in Open and Distributed Learning, 12(3), 98-118.

Cozby, P. C., & Bates, S. C. (2018). Methods in behavioural research. MCGraw-Hill Education.

Cristobal-Fransi, E., Montegut-Salla, Y., Ferrer-Rosell, B., & Daries, N. (2020). Rural cooperatives in the digital age: An analysis of the Internet presence and degree of maturity of agri-food cooperatives'e-commerce. Journal of Rural Studies, 74, 55-66.

Downes, S. (2010). New technology supporting informal learning. Journal of emerging technologies in web intelligence, 2(1), 27-33.

Duke, B., Harper, G., & Johnston, M. (2013). Connectivism as a digital age learning theory. The International HETL Review, 2013(Special Issue), 4-13.

Jinadu, A. T., & Balogun, R. T. (2020). Assessing adoption of synchronous and asynchronous online learning platforms during covid-19 lockdown in Nigeria. Capecomorin Journal,2(4), 96-99.

Jinadu, A. T., Oyaremi, M. K., & Rufai, M. D. (2021). Assessment of the Oyo state teaching service commission interactive learning platforms during covid_19 lockdown period in Nigeria. Interdisciplinary Journal of Educational Research, 3(1), 37-44.

Johnson, B. (2001). Toward a new classification of nonexperimental quantitative research. Educational researcher, 30(2), 3-13.

Lee, H. (2011). Essentials of Behavioural science research: a first course in research methodology. Lulu RDU CentreNC.

Mackness, J., Mak, S., & Williams, R. (2010, May). The ideals and reality of participating in a MOOC. In Proceedings of the 7th international conference on networked learning (Vol. 10, pp. 266-274).

Morenikeji, W. (2006). Research and analytical methods for social scientists, planners and environmentalists. Jos University Press.

Omodan, B. I. (2020). The Vindication of Decoloniality and the Reality of COVID-19 as an Emergency of Unknown in Rural Universities. International Journal of Sociology of Education. 20, 1-26.

Omodan, B. I. (2022). The Connectedness of Posthumanism as a tool for Sustainable Post-COVID-19 Era. In E. O. Adu, M. Fabunmi & V. Mncube (Eds). Education for Sustainable Development in Post COVID-19. Global Education Network.

Omodan, B. I. (2023). Analysis of connectivism as a tool for posthuman university classrooms. Journal Of Curriculum Studies Research, 5(1), 1-12.

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2020). Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital science and education, 2, 923-945.

Schleicher, A. (2020). The impact of COVID-19 on education: insights from education at a glance. OECD Publishing.

Siemens, G. (2004). Elearnspace. Connectivism: A learning theory for the digital age. Elearnspace. org, 14-16.

Siemens, G. (2005). Connectivism: Learning as network-creation. ASTD Learning News, 10(1), 1-28. (2023). What Is Data Collection: Methods, Types, Tools, and Techniques. Data science and business analytics. Free Ebook. Simplilearn Solutions. (2023). Post Covid_19 Data collection methods scale forms app.



How to Cite

Jinadu, A., Akere, O. M., & Balogun, R. T. (2023). Post COVID-19: New breakthroughs and the future of behavioural research data collection. Interdisciplinary Journal of Sociality Studies, 3, 10-18.