Connected Learning are central in supporting research into tertiary education at VU. The resources on this page are intended to give you a ‘head start’ on tertiary education research, and are mapped to broad steps in the research journey.

For specific queries, contact the Educational Research Advisor in Connected Learning, Daniel Loton, at [email protected].

Tertiary Education Research Special Interest Group

Tertiary education research features as an important aspect of VU's strategy, including the VU Digital Strategy.

In 2021, Connected Learning launched a Special Interest Group to support those undertaking research with a focus on aspects of tertiary education. While the primary focus is on the VU Block Model, other educational research is also supported.

The Group has a very broad range of foci, from policy or large-scale educational interventions, to self-studies of individuals improving practice. It is the aim of this Group to facilitate research through staff collaboration, and also by connecting individuals and teams to supporting resources.

A number of mainly informal activities are planned, including a regular casual research writing circle meeting, a peer review program for manuscripts in development, and occasional updates.

The Group is open to any VU staff or research students interested in this broad field of research.

Register your details (VU staff only)

Steps & resources in the educational research process

  • Choose a topic and read prior research
  • Devise research questions and project to address a knowledge gap
  • Form a team, consider team roles, and HDR supervision
  • Consider seeking funding for the project

Resources

Professional development

  • Quality tertiary education Research Design
  • Consider pre-existing VU institutional data
  • Consider pre-registering the project, including questions/aims and hypotheses on the Open Science Framework

Resources

  • Ethical approval
  • Pre-register on Open Science Framework (OSF)
  • Gain Data Insights approval

Resources

  • Create a recruitment / data collection plan
  • If using institutional data, extract, filter and merge to form dataset for analysis

Resources

  • High quality research reporting
  • Seek professional development

Resources

Submit a peer-reviewed journal article and/or conference

Resources

Researching the VU Educational Experience

With a radical new model of tertiary education and a diverse student cohort, VU is in a unique position to contribute knowledge on innovative approaches to enhance student success at University.

Connected Learning has compiled a range of resources (login required) and links to other researcher development opportunities, to support and fast-track research on the educational experience at VU.

Tertiary education research is a substantial field. Jumpstart your knowledge by looking within the following ‘research syntheses’ which have been collated by tertiary education researchers. They provide a ‘birds eye view’ of the field:

A quick supplemental search should be undertaken to identify more recent topical papers. One way is to use Google Scholar, which can list all papers citing a given work, with results filtered to recent years.

As you read each paper, write a short summary of the method and key results relevant to the topic. These will contribute to your eventual literature review. If you use reference management software, such as Endnote, add each paper as you go.

For training on undertaking a more involved systematic literature review with defined search terms and inclusion criteria, keep an eye out on the VU Researcher Professional Development opportunities.

With a surge of interest on the VU Block Model, Connected Learning undertook a comprehensive review and annotated bibliography.

As there are very few studies on block mode learning, the bibliography focuses on what is arguably the most closely related body of literature - intensive mode learning.

VU researchers are stepping in to fill this gap in knowledge on Block Mode learning. See selected publications that investigate aspects of education at VU below.

In addition, Connected Learning have over 50 tertiary education research projects approved through its’ internal micro-project application process.

Depending on the size and scope of your tertiary education research project, and the level of experience and resources available to the team, you may wish to consider seeking internal or external funding to support the research project.

Funding avenues will most likely emphasise either the generation of new knowledge in areas where they are critical gaps – likely to align with competitive research grant schemes; or the more immediate benefits to potentially vulnerable student cohorts – likely to align with philanthropic funding bodies.

Researchers have already been successful in attaining funding from the National Centre for Student Equity in Higher Education on the role of the VU model in enhancing outcomes for STEM students.

All staff at VU have access to the funding source search engine, Research Professional, where you can search for grant opportunities.

Before embarking on writing a bid for research funding, contact the Research Funding team within the Office of Research Services

After you have designed your study, seek ethical approval. All human research in Australia, and at VU, requires ethical approval, consistent with the Australian Code for the Responsible Conduct of Research.

To support relevant research Connected Learning, in co-ordination with the Ethics Chair and Office for Researcher Training, Quality & Integrity, developed and administer an expedited micro-project ethical approval process. Over forty applications have so far been approved, on a variety of topics relating to education at VU.

See relevant forms and process to apply. Note VPN login required.

When planning your study of education at VU, consider pre-existing data sources that may inform your analyses while concurrently minimising participant burden. Higher Education systems generate enormous amounts of data, and studies using this data are sometimes termed 'learning analytics'.

Pre-existing data sources are usually not collected with explicit consent to be used in research, however VU researchers have been successful in obtaining ethical approval to analyse this data. Note that approval is granted on a case-by-case basis.

Example institutional data sources include: student demographics and pathways, assessment results, participation data in the Learning Management System, discussion forum posts, and the Student Evaluation of Teaching and Student Evaluation of Unit (SET/SEU) surveys.

The Information, Analysis and Reporting Department have developed the VU Educational Innovation Datasets Tool for the purpose of researching VU institutional data.

If you'll be collecting new primary data, use VU’s institution-wide subscription to the online survey software, Qualtrics.

Consider that:

  • most surveys of the general public have a 30% response rate
  • in-class recruitment has a higher response rate
  • VU student surveys via email get a very low response rate, text messages may be more effective, and multiple reminders may also help.

From the outset, prepare to share your findings. Depending on your experience in publishing in this field, consider local symposia or conferences, national or international conferences providing peer review sources, and further along the track, aim for a peer-reviewed journal.

Below are just a handful of higher education conferences and journals for you to consider.

Make sure you claim your research outputs on the VU Research Impact system, MORA, and nominate one of the VU priority FOR codes related to higher education. You can check journal rankings on SciMago and contact your Research Librarian for further support.

VU Priority FOR Codes (2018):

  • 1301 Education Systems
  • 1302 Curriculum and Pedagogy
  • 1303 Specialist Studies in Education

Regional focus

Journals
Conferences

International focus

Journals
Conferences

National

Journals

Selected Publications on the VU Block Model

Samarawickrema, G., & Cleary, K. (2021).  Block mode study: Opportunities and challenges for a new generation of learners in an Australian university. Student Success, 12(1), 13-23. 

Kelly K., Klein R., Sinnayah P., Winchester M. (2020). First year University student experiences of block mode intensive learning compared to the traditional university model. International Journal of Innovation in Science Education.

Klein, R., Kelly, K., Sinnayah, P., & Winchester, M. (2020). The block model intensive learning at University favours low achieving students. International Journal of Innovation in Science and Mathematics Education, 27(9).

Loton, D., Stein, C., Parker, P., & Weaven, M. (2020). Introducing block mode to first-year university students: a natural experiment on satisfaction and performance. Studies in Higher Education, 1-23.

Loton, D., Parker, P., Stein, C., & Gauci, S. (2020). Remote learning during COVID-19: Student satisfaction and performance. EdArXIv.

Luguetti, C., & Oliver, K. L. (2020). A transformative learning journey of a teacher educator in enacting an activist approach in Physical Education Teacher Education. The Curriculum Journal.

Oraison, H., Konjarski, L., Young, J., Howe, S., & Smallridge, A. (2020, July). Staff Experiences of Victoria University’s First Year College During the Implementation of Block Mode Teaching. In 6th International Conference on Higher Education Advances  (pp. 45-51). Universitat Politecnica de Valencia.

Samarawickrema, G., & Raponi, K. (2020). A field trip in the first week at university: perspectives from our LLB students. The Law Teacher, 54(1), 103-115.

Tripodi, N., Kelly, K., Husaric, M., Wospil, R., Fleischmann, M., Johnston, S., & Harkin, K. (2020). The Impact of Three‐Dimensional Printed Anatomical Models on First‐Year Student Engagement in a Block Mode Delivery. Anatomical Sciences Education.

Tripodi, N., Kelly, K., Husaric, M., Wospil, R., Fleischmann, M., Johnston, S., & Harkin, K. (2020). Block, engaged? The impact of three‐dimensional printed anatomical models on first‐year student engagement in a block‐mode delivery. Anatomical Sciences Education.

Walsh, C., Mital, A., Ratcliff, M., Yap, A., & Jamaleddine, Z. (2020). A public-private partnership to transform online education through high levels of academic student support. Australasian Journal of Educational Technology, 36(5), 30-45.

Boardman, G., Lawrence, K., & Polacsek, M. (2019). Undergraduate student nurses’ perspectives of an integrated clinical learning model in the mental health environment. International Journal of Mental Health Nursing. 28(1), 96-104.

Downie, C. (2019, September). First year students and research: A constructivist approach. In Proceedings of The Australian Conference on Science and Mathematics Education (p. 31).

Gibbons, J., & Trifkovic, A. (2019). Students as Co-creators in Curriculum Design and Development for Tertiary Education. In Students, Transitions, Achievement, Retention & Success (STARS) Conference.

Howe, S., Konjarski, L., Oraison, H., & Smallridge, A. Trials, Tribulations & Triumphs of the First Year Model. Students, Transitions, Achievement, Retention & Success (STARS) Conference.

Kelly, K. R., & Lock, E. (2019, June). Constructing a Career Mindset in First Year Students: The Building Blocks for Curriculum Design. In 5th International Conference on Higher Education Advances (HEAd'19) (pp. 47-54). Universitat Politècnica València.

Klein, R., Sinnayah, P., Kelly, K., Winchester, M., Rajaraman, G., & Eizenberg, N. (2019). Utilising computer based learning to complement class teaching of gross anatomy. International Journal of Innovation in Science and Mathematics Education. 27(8).

Luguetti, C., & McLachlan, F. (2019). ‘Am I an easy unit?’ Challenges of being and becoming an activist teacher educator in a neoliberal Australian context. Sport, Education and Society, 1-14.

McCluskey, T., Weldon, J., & Smallridge, A. (2019). Rebuilding the first year experience, one block at a time. Student Success, 10(1), 1-16.

McCluskey, T., Weldon, J., & Smallridge, A. (2019). New kids on the Block: Results of a First Year College Initiative.  Students, Transitions, Achievement, Retention & Success (STARS) Conference.

O'Bryan, S. (2019). Quantifying student learning within the ‘zone of proximal development’: Application in an accelerated program.  Students, Transitions, Achievement, Retention & Success (STARS) Conference.

Oraison, H., Konjarski, L., & Howe, S. (2019). Does university prepare students for employment?: Alignment between graduate attributes, accreditation requirements and industry employability criteria. Journal of Teaching and Learning for Graduate Employability, 10(1), 173.

Sidiroglou, F., & Fernandes, N. (2019). The impact of blended learning on student performance in an intensive block mode teaching setting. ICICTE 2019 proceedings.

Boardman, G., Lawrence, K., & Polacsek, M. (2018). Preceptors’ perspectives of an integrated clinical learning model in a mental health environment. International Journal of Mental Health Nursing. 27(5), 1420-1429.

McDonald, T., Ghaith Zakaria, D., & Wilkie, S. (2018). ePortfolio as a method of assessment to transition students through the Block Model. Improving University Teaching International Conference

Tertiary Education Research Methodologies

Designing a tertiary education (TE) research project involves consideration of research methodology: the specific approaches to collecting, analysing, and making sense of data that ultimately produce the key outcome - findings. To support TE research at VU, we’ve compiled sources that give an overview of research design and methodology common in the TE field.

Some methodological considerations are unique to certain research communities, while others are somewhat universal. Methods are often discussed within the broader traditions of quantitative research, that follows empiricism and uses numbers to understand a phenomenon; and qualitative research, which generally takes a constructivist approach to understand how people construe meaning in their experiences.

If you are new to research, or to TE research – don’t worry! Almost every conceivable methodology has been applied and published in TE. In other words, there is no one right way to do research, but a multiplicity of approaches. With an informed application grounded in prior research, with appropriate justification, the method adopted will help to make a meaningful contribution to knowledge.

Many analyses make use of specific software. Rstudio and R packages are commonly used for quantitative research, and is usually free to use for research. VU also has licenses for SPSS (descriptive and inferential statistics), AMOS (structural equation modelling) and NVivo (qualitative analysis), which can be found on the VU SoftwareCentre.

VU has several methodological training opportunities via the Research Professional Development calendar, including some on specific software. We also recommend signing up to the VU research info mailing list for further updates on researcher development (intranet sign-in required). Further enquiries can be directed to the Educational Research Advisor

Here is a list of specific methods applied in the higher education context:

General Higher Education research

Huisman, J., & Tight, M. (2016). Theory and method in higher education research. Emerald.

Perry, R. P., & Smart, J. C. (2007). The scholarship of teaching and learning in higher education : an evidence-based perspective. Springer.

Dewer, J 2018, The scholarship of teaching and learning: a guide for scientists, engineers, and mathematicians, Oxford University Press

Scholarship of Teaching and Learning (SotL) HERDSA online modules

Qualitative

Grounded theory - Draucker, C. B., Martsolf, D. S., Ross, R., & Rusk, T. B. (2007). Theoretical sampling and category development in grounded theory. Qualitative Health Research, 17(8), 1137-1148.

Sampling - Robinson, O. C. (2014). Sampling in interview-based qualitative research: A theoretical and practical guide. Qualitative Research in Psychology, 11(1), 25-41., van Rijnsoever, F. J. (2017). (I can’t get no) saturation: a simulation and guidelines for sample sizes in qualitative research. PLoS One, 12(7), e0181689.

Auto-ethnography - Herrmann, A. F. (Ed.). (2020). The Routledge International Handbook of Organizational Autoethnography. Routledge. (available via library request)

Conversation analysis - Sidnell, J., & Stivers, T. (2013). The handbook of conversation analysis. Wiley-Blackwell.

Practice-based theory - Hofmann, R. (2020). Dialogues with data: generating theoretical insights from research on practice in higher education. In Theory and Method in Higher Education Research. Emerald Publishing Limited.

Case studies - Grootenboer, P., Edwards-Groves, C., & Choy, S. (2017). Practice theory perspectives on pedagogy and education. Praxis, diversity, and contestation.

For studies using raters, observers or coders:

Inter-rater reliability - Gwet, K. L. (2014). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters. Advanced Analytics, LLC. (available on request)

Quantitative

Survey design - Fowler Jr, F. J., & Cosenza, C. (2009). Design and evaluation of survey questions. The SAGE handbook of applied social research methods, 375-412.

Validity and reliability of measures - Wagemaker, H (2020). Reliability and Validity of International Large-Scale Assessment. Springer.

Psychometrics: classical test theory, IRT, SET E-SEM - Marsh, H. W., Guo, J., Dicke, T., Parker, P. D., & Craven, R. G. (2020). Confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and set-ESEM: optimal balance between goodness of fit and parsimony. Multivariate Behavioral Research, 55(1), 102-119., Carlson, J. E. (2020). Introduction to Item Response Theory Models and Applications. Routledge.

Power analysis - Nakagawa, S., & Foster, T. M. (2004). The case against retrospective statistical power analyses with an introduction to power analysis. Acta ethologica, 7(2), 103-108.

Mediation and moderation - Hayes, Andrew F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY: The

Multi-level modelling - Gorard, S. (2003). What is Multi–level Modelling For?. British Journal of Educational Studies, 51(1), 46-63.

Causal modelling in longitudinal data - Zyphur, M. J., Voelkle, M. C., Tay, L., Allison, P. D., Preacher, K. J., Zhang, Z., ... & Diener, E. (2020). From data to causes II: Comparing approaches to panel data analysis. Organizational Research Methods, 23(4), 688-716., Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference. Cambridge University Press.

Randomised controlled trials - Matthews, J. N. (2006). Introduction to randomized controlled clinical trials. CRC Press.

Implementation science - Soicher, R. N., Becker-Blease, K. A., & Bostwick, K. C. (2020). Adapting implementation science for higher education research: the systematic study of implementing evidence-based practices in college classrooms. Cognitive Research: Principles and Implications, 5(1), 1-15.

Meta-analysis - Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87-122.

Mixed or multi-method

Integration - Clark, V. L. P. (2019). Meaningful integration within mixed methods studies: Identifying why, what, when, and how. Contemporary Educational Psychology, 57, 106-111.

Charting usage - Munoz-Najar Galvez, S., Heiberger, R., & McFarland, D. (2020). Paradigm wars revisited: A cartography of graduate research in the field of education (1980–2010). American Educational Research Journal, 57(2), 612-652.

Quality Tertiary Education Research Reporting

Given the wide variety of methodological approaches and associate debates, it is critical to thoroughly report exactly how a research project was undertaken. The guiding principle is that the research publication should describe the method in sufficient detail for others to replicate the study, and hopefully arrive at the same conclusions (if the study is empirical). Thorough reporting will also increase the chances of acceptance during peer review.

It is important to be aware of the growing movement to Open Science, detailed and facilitated by the Centre for Open Science and the Open Science Framework.

There are several issues associated with Open Science, some are methodological, and others relate to the availability of the research outputs to the public. The two most important aspects to consider for your own TE projects are:

  • pre-registration of hypotheses, which provides a public register of the hypotheses before data is collected or analysed (this prevents changing the hypothesis to fit statistical results later)
  • sharing the de-identified dataset so others can re-analyse the data using different methods.

We strongly recommend you pre-register your project, including specific research questions, aims or hypotheses.

Comprehensive reporting of how a study was undertaken is a key issue, not only in TE research, but across all research fields. Many studies are not reported in sufficient detail. This is especially important given many studies in education are subsequently meta-analysed, for which detailed descriptions of the data and method are required.

Check the journal you are aiming for to see if they recommend a particular reporting checklist. The CONSORT (quantitative) or COREQ (qualitative) checklists can help in comprehensive reporting. There is also a mixed-methods appraisal tool for mixed methods research. Finally, the Healey (2019) paper discusses distinct writing styles in SoTL.

  • CONSORT – for Quantitative trials, common in TE in the form of comparative analysis
  • COREQ – a comprehensive checklist for reporting Qualitative studies using interviews or focus groups
  • MMAT (for mixed methods studies)
  • Healey (2019), describing a variety of writing styles for SoTL research

With the rise of research output metrics, there are also wider debates on how to assess quality in research. While these may be more relevant to institutions than individual researchers, it is worthwhile being cognizant of their development.

Firstly, VU has its’ own research impact measurement system, MORA. A larger-scale example is the San Francisco Declaration on Research Assessment, or DORA, which now has many signatories.

Finally, when your draft is complete, consider uploading your manuscript to a pre-print server. These enable you to share results prior to publication in a journal, to show the evolution of the work, and most journals support the use of pre-prints. EdArxIv is one option that is focused on education, but there are other non-specific pre-print servers also, like Springer’s In Review.