Research Proposal – Casualisation of the Academic Workforce and Teaching Quality

Introduction

Over the past three decades the composition of the academic workforce in Australian universities has been transformed. Teaching once delivered predominantly by continuing (ongoing) staff is now heavily reliant on casual and sessional academics engaged, often at short notice, to teach individual units on a semester-by-semester basis. This restructuring, widely termed the casualisation of academic work, has moved from the margins of workforce policy to the centre of national debate, culminating in the Australian Universities Accord, which identified insecure employment as a structural weakness of the sector (Universities Accord, 2024). Despite this attention, the consequences of casualisation for the core activity of universities, namely the quality of teaching that students receive, remain surprisingly under-examined in the Australian context.

This proposal sets out a 15-month mixed methods study that tests whether, and through what mechanisms, the casualisation of academic labour is associated with teaching quality in Australian public universities. It draws on the job demands-resources framework to specify the pathways through which employment conditions may shape teaching, and is intended to produce evidence relevant to workforce planning, quality assurance and the implementation of recent national policy.

Background and Problem Statement

The growth of casual academic work

Casual academics have become a structural feature of Australian higher education rather than a marginal supplement to a stable, tenured workforce. Department of Education staff data show that casual and fixed-term appointments account for a large and growing share of the academic headcount, and the National Tertiary Education Union (NTEU) has estimated that casual staff deliver a substantial proportion of undergraduate teaching in many disciplines (Department of Education, 2023; NTEU, 2021). Casual employment is common across the wider Australian labour market, where around one in five employees have no paid leave entitlements and are classified as casual (Australian Bureau of Statistics [ABS], 2023). What distinguishes the academic case is the combination of high qualifications, fragmented and unpredictable contracts, and the centrality of the work to a regulated public good.

Insecurity and underpayment

The insecurity attached to sessional work is well documented. Contracts are typically short, offered close to the start of semester, and carry no guarantee of re-engagement, which transfers the burden of income uncertainty onto individuals who frequently hold doctoral qualifications (Kimber, 2003; May et al., 2013). These conditions have been compounded by widespread underpayment. Since 2020 the Fair Work Ombudsman has pursued a series of Australian universities over the systematic underpayment of casual academic staff, with institutions repaying substantial sums for unpaid marking, preparation and consultation (Fair Work Ombudsman, 2023). Such cases expose a structural feature of piece-rate casual pay: much of the labour of teaching, including curriculum preparation and student consultation, is performed outside the paid contact hours that casual rates nominally cover.

Problem statement

These conditions plausibly bear on students. Where teaching is delivered by staff who lack office space, continuing access to institutional systems, paid preparation time, or any expectation of returning the following semester, the continuity and depth of the student learning experience may be compromised (Ryan et al., 2013). The Higher Education Standards Framework administered by the Tertiary Education Quality and Standards Agency (TEQSA) requires providers to ensure that staffing is sufficient in number and appropriately qualified to deliver quality learning outcomes (TEQSA, 2021), which places the relationship between employment mode and teaching quality squarely within the regulatory field. The problem this study addresses is that, despite sustained policy concern, there is little Australian evidence that empirically links casualisation to teaching quality through a clearly specified mechanism. Description of casual academics’ experience is plentiful; explanation of its consequences for teaching is not.

Aim and Research Questions

The aim of this study is to examine the relationship between the casualisation of academic work and teaching quality in Australian universities, and to identify the mechanisms through which employment conditions shape teaching practice. Three research questions follow:

1. To what extent does employment precarity among Australian academics predict teaching quality, measured through self-reported teaching behaviours and unit-level student experience data?
2. Do job demands and job resources mediate the relationship between casualisation and teaching quality?
3. How do sessional academics themselves understand the ways in which their employment conditions shape their teaching practice?

Literature Review

Precarious academic labour in Australia

Australian scholarship has long characterised the academic workforce as divided between a secure core and an expanding, insecure periphery (Kimber, 2003). May et al. (2013) locate this division within labour market segmentation theory, arguing that casual academic work forms a distinct secondary segment marked by low security, weak career progression and limited institutional voice. Qualitative studies document the lived consequences: casual staff report exclusion from decision-making, curriculum design and collegial networks, and describe themselves as marginalised within their own departments (Ryan et al., 2013). These experiences fall disproportionately on women and early-career academics, who are over-represented in sessional roles and frequently lack even basic infrastructure such as a desk or reliable access to campus systems (Crimmins, 2016). This work is rich in description, yet it documents the experience of casual staff rather than testing the consequences of casualisation for teaching outcomes, leaving the link to educational quality largely assumed.

Conceptualising and measuring teaching quality

Teaching quality is a contested, multidimensional construct. In Australian practice it is most visibly operationalised through centralised student experience surveys, which capture satisfaction at unit and teacher level but are confounded by class size, discipline, mode of delivery and cohort characteristics, and measure satisfaction rather than learning directly. This study therefore treats teaching quality as a composite of observable practices, including preparation, timeliness and quality of feedback, and availability to students, combined with unit-level student experience data so that neither source carries the full evidentiary burden.

The job demands-resources model

The job demands-resources (JD-R) model offers an established account of how work characteristics translate into performance and wellbeing outcomes (Bakker & Demerouti, 2017). Every role, it holds, comprises demands that require sustained effort and resources that support goal achievement and personal growth. Two pathways follow: a health-impairment pathway in which excessive demands deplete energy and produce strain and burnout, and a motivational pathway in which adequate resources foster work engagement. Casualisation maps onto both. It plausibly raises demands, through workload compression, role ambiguity, unpaid preparation and chronic job insecurity, while simultaneously lowering resources, by restricting autonomy, professional development, collegial support and access to institutional infrastructure. Positioning teaching quality as the performance outcome of these two pathways gives the model clear explanatory purchase on the research problem.

Synthesis and gap

The three literatures have developed largely in parallel. Research on precarious academic labour documents experience without measuring educational consequences; scholarship on teaching quality rarely attends to the employment status of the teacher; and JD-R research seldom examines academic precarity as a demand in its own right. No Australian study has yet combined validated measurement of demands, resources and teaching quality with the employment conditions of a national academic sample and independent student experience data. Addressing that gap is the contribution of this study.

Conceptual Framework

The conceptual model integrating these literatures is shown in Figure 1. Drawing on the JD-R model, casualisation is theorised to influence teaching quality indirectly, through two mediating pathways. Casualisation raises job demands, which erode teaching quality through psychological strain, and it reduces job resources, which support teaching quality through work engagement. Teaching quality in turn shapes the student experience captured in institutional surveys. The model yields three testable hypotheses: (H1) employment precarity is negatively associated with teaching quality; (H2) the relationship is mediated by job demands acting through psychological strain; and (H3) the relationship is mediated by job resources acting through work engagement.

Methodology

Design

The study will employ an explanatory sequential mixed methods design, in which a quantitative phase is followed by a qualitative phase that explains and elaborates the statistical findings (Creswell & Plano Clark, 2018). The quantitative phase establishes the strength and structure of the relationships specified in Figure 1; the qualitative phase then investigates how sessional academics interpret and enact those relationships in their own teaching.

Participants and sampling

The quantitative phase will recruit a target sample of approximately 600 academics through professional associations, disciplinary networks and staff mailing lists, using stratified sampling to ensure adequate representation of both sessional and continuing staff across broad discipline clusters and across research-intensive, metropolitan and regional universities. Divided across roughly equal strata of sessional and continuing staff, 600 respondents yield about 300 cases per group, exceeding the number required to detect a small mediated effect with adequate power and leaving margin for incomplete responses. Unit-level student experience scores will be obtained in de-identified, aggregate form to provide a teaching-quality indicator that does not rely on self-report. The qualitative phase will comprise 20 semi-structured interviews with sessional academics purposively sampled from consenting survey respondents to capture variation in discipline, career stage and institutional setting.

Instruments and measures

Wherever possible the survey will use established instruments with published psychometric evidence, supplemented by short custom scales for constructs specific to the academic context. Table 1 summarises the constructs, their indicative measures and their function in the model.

Table 1: Constructs, indicative measures and their function in the model

Note. The self-report teaching-quality items and the unit-level student experience scores are treated as complementary indicators of a single latent outcome, so that neither self-report bias nor the known confounds of student surveys dominate the estimate.

Analysis

Quantitative data will be analysed in two stages. Descriptive and correlational analysis will characterise the sample and the bivariate relationships, after which structural equation modelling will test the measurement model and the mediation hypotheses specified in Figure 1, with bias-corrected bootstrapped confidence intervals used to evaluate the indirect effects. Contextual variables including discipline, institution type, gender and years of casual engagement will be entered as covariates. The qualitative interviews will be transcribed and analysed using reflexive thematic analysis, with codes developed initially around the constructs in the model and then extended inductively (Braun & Clarke, 2021). Integration will occur through a joint display that sets the statistical pathways alongside the interview themes, allowing convergence and divergence between the two strands to be interpreted as meta-inferences.

Ethical Considerations

Approval will be sought from the administering university’s Human Research Ethics Committee (HREC), and the study conducted in accordance with the National Statement on Ethical Conduct in Human Research. Three issues require particular attention. First, the subject matter is sensitive in relation to the employer: participants may reasonably fear that candid comment on their conditions could jeopardise future re-engagement. Recruitment will therefore be conducted by the research team rather than by managers or heads of school, participation will be voluntary and unremunerated, and no individual’s responses or participation status will be disclosed to any institution. Second, confidentiality will be protected through de-identification at the point of data entry, secure storage of linked data, and reporting only in aggregate, with no institution or individual named in any output. Third, because the unit-level student experience data are institutionally held, their use will be governed by formal data-sharing agreements that preclude re-identification of individual teachers. Informed consent, the right to withdraw without consequence, and access to support-service information will be standard.

Project Timeline

The study will be conducted over 15 months, as set out in Table 2. Sequencing the qualitative phase after preliminary quantitative analysis is intrinsic to the explanatory sequential design, since interview sampling and questioning are informed by the survey results.

Table 2: Fifteen-month project timeline

Significance of the Study

For the sector, the study offers evidence that current workforce debate largely lacks: a test of whether, and how, employment mode is related to the quality of teaching students actually receive. If precarity predicts lower teaching quality through the demands pathway, the finding would strengthen the case, advanced by the Universities Accord (2024), for reducing insecure employment as a matter of educational quality rather than industrial fairness alone. For regulators, it would clarify how the staffing adequacy provisions of the Higher Education Standards Framework relate to measurable teaching outcomes (TEQSA, 2021). For institutions, identifying which resources most strongly protect teaching quality would direct limited support towards the interventions likely to matter most for sessional staff. Theoretically, the study extends the JD-R model to precarious academic labour by treating employment insecurity itself as a job demand.

Limitations and Delimitations

Several constraints qualify the design. The survey is cross-sectional and cannot establish causal ordering, although the use of independent student experience data reduces reliance on common-method self-report. Self-report measures of teaching behaviour are vulnerable to social desirability, and aggregate student experience scores carry their own well-known confounds. Voluntary participation raises the possibility of self-selection, with the most aggrieved or most engaged casual staff over-represented, and access to institutional student experience data may prove uneven across universities. The study is delimited to the teaching function and to public universities, excluding the research and service dimensions of academic work and the private higher education sector. A longitudinal extension would be the logical next step.

Conclusion

The casualisation of academic work is now a defining feature of Australian higher education, and one that national policy has belatedly recognised as a structural concern. Yet the debate has proceeded largely without evidence on the question that matters most for students: whether insecure employment is associated with the quality of teaching. This proposal sets out a feasible 15-month mixed methods study that measures the employment conditions, job demands, job resources and teaching quality of a national academic sample, links them to independent student experience data, and explains the resulting patterns through the accounts of sessional academics themselves. By grounding the analysis in the job demands-resources model and in the Australian regulatory context, the study aims to convert a widely held concern into empirical evidence capable of informing workforce planning, quality assurance and national policy.

References

Australian Bureau of Statistics. (2023). Working arrangements, Australia. Australian Bureau of Statistics.

Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273-285.

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.

Crimmins, G. (2016). The spaces and places that women casual academics (often fail to) inhabit. Higher Education Research & Development, 35(1), 45-57.

Department of Education. (2023). Selected higher education statistics: Staff data. Australian Government.

Fair Work Ombudsman. (2023). Compliance and enforcement outcomes in the higher education sector. Commonwealth of Australia.

Kimber, M. (2003). The tenured ‘core’ and the tenuous ‘periphery’: The casualisation of academic work in Australian universities. Journal of Higher Education Policy and Management, 25(1), 41-50.

May, R., Peetz, D., & Strachan, G. (2013). The casual academic workforce and labour market segmentation in Australia. Labour & Industry, 23(3), 258-275.

National Tertiary Education Union. (2021). Insecure work in Australian universities. National Tertiary Education Union.

Ryan, S., Burgess, J., Connell, J., & Groen, E. (2013). Casual academic staff in an Australian university: Marginalised and excluded. Tertiary Education and Management, 19(2), 161-175.

Tertiary Education Quality and Standards Agency. (2021). Higher Education Standards Framework (Threshold Standards) 2021. Australian Government.

Universities Accord. (2024). Australian Universities Accord: Final report. Department of Education.