II Data School Research

II Data School Research

Dr. Tamaro J. Green

II Data School researched applications of research in education. As a team of researchers, II Data School has the capabilities to support qualitative content analysis in education to support the fields of information security, finance, climate, and medicine. Qualitative content analysis may also provide guidance for improving governance to education research. Scalabrin Bianchi, Dinis Sousa, and Pereira (2021) conducted a design science research study for governance in education. Simarmata, Djohar, Purba, and Juanda (2018) presented the design of a blended learning environment. Gläser-Zikuda, Hagenauer, and Stephan (2020) reviewed qualitative content analysis in education within convergent, explanatory sequential, and exploratory sequential designs. Leck and Wood (2013) suggested trust between mentors and mentees for online mentoring.

An independent review, or an audit, of education trends may support improvements to educational institutions. Collaborating on research provides the ability for researchers to define parameters for the research and allows the research to develop structure and maintain validity and verifiability. Common themes are identified in the research for coding analysis.

An inter-observer agreement among a collaboration of researchers promotes discussion on differences until there is a consensus. Hill et al. (2005) provided a review of consensual qualitative research studies and provide recommendations to improve the consensus process and study alternative teams. Hill et al. (2005) suggested that consensual qualitative research may have advantages for studying events hidden from public view. A consensual qualitative research in technology labour may provide revelations about opaque technology work environments. McHugh, Farley, and Rivera (2020) provided a qualitative exploration research study of shift work experiences of labour in the manufacturing sector.

Artificial intelligence may have applications for developing public and private sector responses to the global pandemic in changing education systems (van der Schaar et al., 2020). van der Schaar et al. (2020) describe how public and private sectors can cooperate to apply artificial intelligence in developing plans and responses to pandemics. Valdova, Penna, Tobin, and Fishbein (2020) discuss the importance of focused data collection to support identifying treatments in the pandemic.

The global pandemic has also increased the difficulty of providing effective learning environments in education (Li, An, & Ren, 2020). Policy makers may be able to apply artificial intelligence in increasing resources to support educational environments (van der Schaar et al., 2020). Li et al. (2020) study the habits of educators and students in online learning environments during the pandemic. Policy makers may need to change the model of approaching the effects on education from a data protection issue to an ethical issue (Kazim & Koshiyama, 2020). Kazim and Koshiyama (2020) discuss the challenges in applying a data protection lens to the complex assessment of ethical issues in artificial intelligence. II Data School continues to research how to support academia and industry in improving educational institutions.

References:

Gläser-Zikuda, M., Hagenauer, G., & Stephan, M. (2020). The potential of qualitative content analysis for empirical educational research. Forum Qualitative Sozialforschung, 21(1).

Hill, C. E., Knox, S., Thompson, B. J., Williams, E. N., Hess, S. A., & Ladany, N. (2005). Consensual qualitative research: An update. Journal of Counseling Psychology, 52(2), 196–205.

Kazim, E., & Koshiyama, A. (2020). The interrelation between data and AI ethics in the context of impact assessments. AI and Ethics. doi:10.1007/s43681–020–00029-w

Leck, J. D., & Wood, P. M. (2013). Forming trust in e-mentoring: A research agenda. American Journal of Industrial and Business Management, 3, 101–109.

Li, M., An, Z., & Ren, M. (2020). Student-centred webcast + home-based learning model and investigation during the COVID-19 epidemic. Inteligencia Artificial, 23(66), 51–65.

McHugh, M., Farley, D., & Rivera, A. S. (2020). A qualitative exploration of shift work and employee well-being in the US manufacturing environment. Journal of Occupational and Environmental Medicine, 62(4).

Scalabrin Bianchi, I., Dinis Sousa, R., & Pereira, R. (2021). Information technology governance for higher education institutions: A multi-country study. Informatics, 8(2). doi:10.3390/informatics8020026

Simarmata, J., Djohar, A., Purba, J., & Juanda, E. A. (2018). Design of a blended learning environment based on Merrill’s principles. Journal of Physics: Conference Series, 954.

Valdova, V., Penna, S., Tobin, M., & Fishbein, J. (2020). The value of real-world evidence for clinicians and clinical researchers in the coronavirus crisis. Gazette of Pharmacology and Clinical Research, 1(1).

van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., . . . Ercole, A. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning. doi:10.1007/s10994–020–05928-x