Abstract
The introductory chapter sets the context for the book. Since the onset of Covid-19, students, teachers and universities have had to adopt online and blended learning, often with little or no experience, expertise, or models of good practice to draw upon. The chapter then provides an overview of the book. The first part of the book shows how some universities have expanded and diversified their student intake by shifting towards a contemporary model of admission and course delivery, including the availability of online learning. As a result, they gained experience and expertise in online and blended learning prior to the onset of Covid. The second part of the book examines the role of student support services in promoting the retention and success of online and blended learners. The third part presents a model, tested with Structural Equation Modelling (SEM), of how four elements of online pedagogy can generate a supporting online environment that prompts the formation of virtual learning communities. Two chapters in this part of the book provide detailed qualitative illustrations of how teachers can put the model into practice for online and blended learners. This introductory chapter provides overall details of the student interviews which generated the data for most of the chapters in the book. The introductory chapter explains SEM in a way that a non-specialist will be able to understand.
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Notes
- 1.
See Hox and Bechger (1998) for an overview of model identification.
- 2.
Note: Latent Variables are often referred to as “Factors”.
- 3.
For example, various path tracing rules have been proposed (e.g. Wright [1934])—however more recent developments (e.g. PLS SEM) have rendered many of these obsolete.
- 4.
This form of relationship is often difficult to conceptualise for those less familiar with quantitative methodologies. One way to do so is to consider the following example: in the summer months, people tend to spend more time outside and consume more ice-cream however to hypothesise that one of these phenomena directly influences the other would be contentious at best.
- 5.
It is worth noting that in some instances (e.g. where an item on a questionnaire is ‘reverse coded’) these coefficients will have a range of −1 to 0 however this phenomena does not occur within the current volume.
- 6.
This measure is more formally referred to as Average Variance Extracted (AVE).
- 7.
More formally, the squared correlation coefficient should exceed the AVE for each latent variable.
- 8.
These are often simply referred to as SEM models in the literature however we have adopted an alternative terminology to avoid potential confusion.
References
Bada, S. O., & Olusegun, S. (2015). Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education, 5(6), 66–70. https://doi.org/10.9790/7388-05616670
Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802–1811. https://doi.org/10.1177/1049732316654870
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Bryman, A., Becker, S., & Sempik, J. (2008). Quality criteria for quantitative, qualitative and mixed methods research: A view from social policy. International Journal of Social Research Methodology, 11(4), 261–276. https://doi.org/10.1080/13645570701401644
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage.
Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage.
Department of Education, Employment and Training (DEET). (1990). A fair chance for all. AGPS.
Dziuban, C. D., Hartman, J. L., & Moskal, P. D. (2004). Blended learning. Educause Center for Applied Research Bulletin, 2004(7), 1–44.
Eldh, A. C., Årestedt, L., & Berterö, C. (2020). Quotations in qualitative studies: Reflections on constituents, custom, and purpose. International Journal of Qualitative Methods, 19. https://doi.org/10.1177/1609406920969268
Elliott, V. (2018). Thinking about the coding process in qualitative data analysis. The Qualitative Report, 23(11), 2850–2861. https://doi.org/10.46743/2160-3715/2018.3560
Fusch, P. I., & Ness, L. R. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report, 20(9), 1408. https://doi.org/10.46743/2160-3715/2015.2281
Ganapathy, M. (2016). Qualitative data analysis: Making it easy for nurse researcher. International Journal of Nursing Education, 8(2), 106–110. https://doi.org/10.5958/0974-9357.2016.00057.X
Hesse-Biber, S. N., & Leavy, P. (2010). The practice of qualitative research. Sage.
Hox, J., & Bechger, T. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354–373.
Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031
Kelloway, K. (1995). Structural equation modelling in perspective. Journal of Organizational Behaviour, 16(3), 215–224.
Kember, D., & Ellis, R. A. (Eds.). (2022). Admission and success for low SES university students: Report on a HEPPP 2018 National Priorities Pool Project. Department of Education and Training.
Kennedy, D. M. (2016). Is it any clearer? Generic qualitative inquiry and the VSAIEEDC model of data analysis. The Qualitative Report, 21(8), 1369–1379. https://doi.org/10.46743/2160-3715/2016.2444
Lindgren, B. M., Lundman, B., & Graneheim, U. H. (2020). Abstraction and interpretation during the qualitative content analysis process. International Journal of Nursing Studies, 108, 103632. https://doi.org/10.1016/j.ijnurstu.2020.103632
Lochmiller, C. R. (2021). Conducting thematic analysis with qualitative data. Qualitative Report, 26(6), 2029–2044. https://doi.org/10.46743/2160-3715/2021.5008
Melis Cin, F., Madge, C., Long, D., Breines, M., & Tapiwa Beatrice Dalu, M. (2021). Transnational online research: Recognising multiple contexts in Skype-to-phone interviews. Qualitative Research, (advance online publication), 14687941211024824. https://doi.org/10.1177/14687941211024824
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
Roulston, K. (2018). Triangulation in qualitative research. Retrieved from https://qualpage.com/2018/01/18/triangulation-in-qualitative-research/
Schumacker, R., & Lomax, R. (2010). Structural equation modeling. Routledge.
Sturges, J. E., & Hanrahan, K. J. (2004). Comparing telephone and face-to-face qualitative interviewing: A research note. Qualitative Research, 4(1), 107–118. https://doi.org/10.1177/1468794104041110
Wright, S. (1934). The method of path coefficients. The Annals of Mathematical Statistics, 5(3), 161–215.
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Kember, D., Trimble, A., Hicks, D. (2023). Introduction. In: Kember, D., Ellis, R.A., Fan, S., Trimble, A. (eds) Adapting to Online and Blended Learning in Higher Education. Springer, Singapore. https://doi.org/10.1007/978-981-99-0898-1_1
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