Work-integrated learning (WIL) is recognised as a valuable pedagogical strategy for learning, and providing students with work experience during their degree program, thereby facilitating their transition into meaningful work post-graduation ( Chillas et al., 2015 ; Jackson et al., 2015 ). WIL can positively impact students’ motivation, engagement and confidence through opportunities to apply theory to practice, enhancement of employment prospects through development of key employability and citizenship skills (e.g. teamwork, problem solving, cultural sensitivity) and building of professional networks ( Jackson, 2013 , 2014 ; Sachs et al., 2017 ; Smith et al., 2014 ). WIL programs are generally based on the reciprocity principle, with WIL activities designed to benefit all stakeholders, including host organisations and their clients and/or communities ( Sachs & Clark, 2016 ) through completion of a needed project, addressing recruitment needs, and providing access to the latest knowledge ( AWPA, 2014 ; Jackson et al., 2015 ; Sachs et al., 2017 ).

Despite the benefits, such programs are recognised as being more time consuming to design and deliver, with implications for the workload of staff teaching and supporting them ( Barrett & Barrett, 2008 ). This is an increasingly important issue given the growth of WIL both within Australia and internationally ( Clark et al., 2014 ). Not surprisingly, workload has been identified as a major challenge to the sustainability of WIL programs ( DeClou et al., 2013 ; Jovanovic et al., 2018 ). When properly resourced, WIL can be a costly endeavour ( Orrell, 2004 , Clark, 2016 ), however, if under-resourced it has the potential to contribute to staff burnout, with excessive workload demands in academic environments impacting on program quality and staff well-being ( Bezuidenhout, 2015 ; Melin et al., 2014 ).

In this respect, WIL has several parallels to online, distance and blended delivery modes, and workload studies of these pedagogical approaches also provide insight into WIL workload considerations. Shared issues include increasing accountability, governance responsibilities, student support demands, pressures to be available 24/7 and the need to redesign curricula for diverse models of delivery ( Bezuidenhout, 2015 ). Multiple role expectations involved in online learning have been linked to increased stress ( Briggs, 2005 ), and changing work roles can have significant implications for the wellbeing and mental health of academics ( Bezuidenhout, 2015 ). It follows that academics involved in WIL delivery may also be at risk of burnout if workload issues are not addressed.

Until recently, little empirical work had systematically investigated the lived experience of practitioners that WIL courses were more time consuming and resource intensive to design, deliver and support than traditional classroom-based courses. The absence of such data makes estimating resourcing requirements, costs and skill sets associated with the design and delivery of WIL experiences difficult ( Lomax-Smith et al., 2011 ; Tallantyre, 2008 ). To address this gap in the literature, and to pro-actively address sustainability issues for a new whole-of-university WIL initiative, a three-year study was initiated at an Australian University to collect data on the amount of work and types of tasks involved in teaching, administering, and supporting WIL courses ( Clark et al., 2014 ). This paper reports on data collected from the project, focusing specifically on the amount of time academics spend on the design/delivery of WIL courses, as well as the types of tasks undertaken by these staff. Some preliminary findings have previously been published elsewhere ( Bilgin et al., 2017 ; Rowe et al., 2014 , 2016 ).

At the time the study was initiated (in 2012) extant literature had established that WIL was a unique approach to teaching and learning which presented a number of challenges to university staff ( McLennan & Keating, 2008 ). Firstly, it involved different (and often new) approaches to curricula and pedagogy, which needed to integrate theoretical, professional and experiential models of learning, with less emphasis on teachers controlling the learning process and more on students’ self-management of learning ( McLennan & Keating, 2008 ).

Secondly, staff were required to fulfil more complex administrative and pastoral responsibilities ( Bates, 2011 ; Bulot & Johnson, 2006 ; Johnston, 2007 ; Sattler et al., 2011 ), with some WIL tasks blurring traditional boundaries between teaching and administrative roles in higher education ( Barrett & Barrett, 2008 ; Bates, 2011 ; Paewai et al., 2007 ; Whitchurch, 2008 ). Examples of tasks considered unique to WIL include recruiting WIL partners, relationship building, administering WIL service agreements, managing student learning contracts, providing career/employment counselling, mentoring students and developing WIL-related curricula ( Bates, 2011 ; DeClou et al., 2013 ). Because some of these are undertaken in addition to regular duties, they are often not recognised by academic workload models ( Emslie, 2011 ), and subsequently underestimated in the time it takes to do them ( Bates, 2011 ) in a similar way to other service roles ( Kenny & Fluck, 2014 ). This further supports the observation that traditional workload models are not well suited to dealing with the changing nature of tertiary teaching ( Dekeyser, et al., 2014 ).

Despite these realisations, specific empirical studies investigating workload in WIL program delivery remain sparse. Bates (2010 , 2011) is an exception, identifying a range of activities that staff working in WIL programs are expected to do as part of, or in addition to, their regular duties. Some general studies on academic workload in higher education acknowledge the time-consuming nature of WIL ( Barrett & Barrett, 2008 ) but have not attempted to measure it. Bates (2010 , 2011) in Australia and Peters et al. (2012) in Canada both identified a range of WIL related tasks/activities, however, neither measured workload hours.

Macquarie is a large metropolitan university located in Sydney, Australia. In 2019 it enrolled over 45,000 students and employed around 3800 staff ( Macquarie University, 2019 ). The university offers a broad suite of programmes covering business, science, arts, medicine, health and human sciences. Historically, the university did not have a widespread tradition of WIL (with a small number of exceptions) until the introduction of the PACE (Professional and Community Engagement) program in 2012. This initiative now provides WIL opportunities to all undergraduate students across the university, regardless of discipline or a student’s academic standing. Each year over 8500 students undertake a WIL project with one of 1000-plus industry and community partners through one of 80-plus purpose-designed, discipline-based PACE coursework units. These units cater for a diversity of delivery modes including internships, remote/virtual consultancy projects, field trips, community development projects, campus-based group projects, research and evaluation.

The scope, scale, diversity and distributed nature of its integration in the curriculum distinguish PACE in the Australian higher education sector, with the program winning a range of national awards for teaching excellence and employability enhancement. Despite their clear advantages in enabling universal access to WIL for students at Macquarie, the scope, scale and diversity of PACE were also identified as factors likely to impact staff workload associated with delivery of the program. It was therefore critical that this research captured the diversity of delivery modes and disciplines to determine their differential impact on staff workload (if any).

Studies of academic workload have tended to rely on self-report survey data ( Bentley & Kyvik, 2011 ; Jordan, 1994 ; Kyvik, 2012 ). This type of data is easy to collect quickly, particularly from large numbers of participants. It has, however, been critiqued for potentially generating inflated results if staff exaggerate workload ( Tight, 2010 ) and being susceptible to recall error ( Bentley & Kyvik, 2011 ). That said, the latter factor could also make under-estimation of workload as or more likely, with participants forgetting to add up the “quick calls/emails” they do between other tasks that don’t get recalled/recorded as easily as blocks of time working on a particular task. While alternative methods were considered by the research team (e.g., random time sampling methods), WIL workload can fluctuate from week to week over the duration of a teaching session ( Jovanovic et al., 2018 ), and it was important for the study to inter alia ascertain which periods were busiest for WIL course convenors. As a result, the survey-based diary approach outlined below was preferred as it would enable the most comprehensive data capture.

Our research contributes much needed empirical data to an under-researched and complex area of learning and teaching in higher education. As recently noted by Jovanovic et al. (2018) “further investigation is timely into what engagement in WIL entails for academic staff, and how it impacts upon their work, workload, professional engagement and identity” (p. 94), with “little work… done within the literature to move beyond calling attention to this issue” ( Jovanovic et al., 2018 : p. 104). Our data supports and extends this, and previous scholarship, by providing evidence of the considerable amount of time it takes to design and deliver quality WIL, and the variability of workload across WIL courses, which represents a major challenge to the sustainability of WIL programs ( DeClou et al., 2013 ; Jovanovic et al., 2018 ) unless it is adequately addressed in workload models and other University resourcing frameworks. Key differentiators (drivers) of WIL workload and its variability include unit characteristics (e.g. class size and responsibility for partner sourcing), along with factors specific to and/or associated with the nature of WIL as a mode of delivery, e.g., additional risk, administrative and partner-related tasks; individual case management of students; increased time invested in learner assessment; and the substantial preparatory and follow-up work that takes place outside formal semester (i.e. hidden workload).

An earlier analysis of interview data ( Rowe et al., 2014 ) revealed some workload tasks in WIL units were perceived by convenors to be the same as those involved in classroom-based teaching, others had workload increased as a result of WIL, and some were unique to WIL courses: reflecting the findings of other studies ( Bates, 2010 , 2011 ; DeClou et al., 2013 ). Quantitative analysis reported in the present paper extends that finding, with workload found to vary systematically across different tasks. While assessment of student learning was the single biggest contributor to course convenor workload in WIL during the teaching period, other studies, e.g., Jovanovic et al. (2018) point to administration as the most dominant aspect. Yet other studies have emphasised aspects of work associated with building industry relationships ( Rook, 2017 ) and supporting students and industry partners ( Bates, 2011 ). The latter findings are not necessarily inconsistent with those of the study reported here, as many of these administrative and partner-sourcing tasks are likely to have been undertaken prior to the commencement of semester, a period during which, this study shows, up to half of the academic workload involved in a WIL unit is incurred. Our study also shows that the workload involved in such administrative and relational tasks also continues throughout the teaching period.

Perhaps it is more important to note that although assessment of student learning and administration can be part of any university course, the nature of these activities for a WIL unit is often profoundly different. This is due to the resource-intensive individual case management of students in many WIL courses, as well as the direct involvement from a third stakeholder, an external partner, who often needs to be sourced by the university and whose involvement raises the stakes and time commitment associated with a range of teaching-related tasks. Extra workload in assessment for example, might be due both to the more individualised nature of assessment in WIL units (e.g. with convenors investing additional time on feedback to ensure student work is presented to the highest standards to external partners ( Bilgin et al., 2017 )) and to the need to support industry partners in assessing students’ professional competence ( McNamara, 2011 ), and following up to ensure the timely return of industry-based assessments ( Bates, 2010 ).

Interestingly, Jovanovic et al. (2018) report that administration and teaching demands contributed to a lack of time for “supporting inquiry learning and our relational pedagogical work, in response to the administration and teaching demands of coordinating WIL effectively within the course” (p. 104). This theme did not come up in our research, however a lack of time to pursue research was a recurrent theme in interviews with participants, and has been noted in other literature ( Bates, 2011 ). This is important given research activities are given more recognition status in academia than that given to forming partnerships with industry and sustaining WIL quality ( Tuinamuana, 2016 ), and thus more directly linked to academic advancement, including promotion.

Another key differentiating factor between workload in WIL and traditional classroom-based courses is the continuous nature of WIL workload, with the lead and lag times associated with each WIL unit providing little respite in work intensity between formal semesters. The building and sustaining of partnerships with community, the development of placements for future student cohorts, and the mentoring of other WIL university-staff and their situational support of students and community partners are tasks that continue throughout the year, contributing to the continuous workload. So too do the desire and pressures to support the “availability and accessibility” of WIL opportunities at all times of the year, which vary widely depending on industry needs, a factor which has been identified as a contributor to workload in other studies ( Jovanovic et al., 2018 ).

Finally, there are the added emotional, pastoral care and reputational risk responsibilities borne by convenors of WIL courses ( Bates, 2010 , 2011 ) which, like many other elements of WIL, are rarely recognised in academic workload models ( Emslie, 2011 ; Tuinamuana, 2016 ). A recent Australian study, for example, reported a “high mental load” associated with managing WIL students, particularly international students, and those with mental health issues ( Wenham et al., 2019 : p. 1033). The hidden nature of this work, and its institutional undervaluation ( Clark et al., 2014 ; Jovanovic et al., 2018 ; Patrick and colleagues, 2008 ), if left unchecked, can lead to staff burnout ( Bezuidenhout, 2015 ; Melin et al., 2014 ). Bates (2010) for example, reported that some WIL staff wanted to change their course responsibilities or administrative positions due to burnout, a perceived lack of support, and the degree of additional responsibility involved. Wenham et al. (2019) similarly note reasons for WIL academic advisors leaving employment include “high workload, inadequate pay and lack of professional autonomy” (p. 1032). These, coupled with higher levels of emotional labour as evident through our interview data, suggest this area may warrant further investigation in WIL, particularly as university lecturers report relatively high levels of emotional labour in comparison to other occupations anyway (including those where emotional labour is prevalent) ( Berry & Cassidy, 2013 ).

The methodology used in this research has a number of limitations, some of which have been reported previously ( Clark et al., 2014 ; Rowe et al., 2016 ). These include challenges around: ensuring data quality (e.g. staff over or under estimating workload), capturing the complexity of workload (e.g. clarifying/interpreting tasks), identifying all potential drivers of workload (particularly those related to student characteristics due to ethics restrictions relating to obtaining data), reluctance of staff to partake in the research due to time restrictions or the sensitive nature of the research and being conducted in a single university. Finally, the resourcing required to administer surveys to participants was considerable, however deemed necessary to ensure data quality.

Data reported in this study are important for informing resourcing and staffing decisions underpinning offering of WIL programs in universities. The sustainability of WIL programs depends on understanding different models of WIL and their impact on staff workload—the importance of adequate resourcing at an institutional level has been recognised in several recent reports ( Sachs et al., 2017 ). WIL workload needs to be formally recognised in academic workload models, with clearer recognition and articulation of expectations around specific workload tasks. This requires “a reassessment of academic work roles and expectations in ways that recognise the distinctiveness and value of WIL” ( Emslie, 2011 : pp. 41-42). Given the reported under-valuing and under-recognition of WIL in the literature ( Emslie 2011 ; Orrell et al., 1999 ; Wenham et al., 2019 ), the involvement of academic staff in this process of effective workload allocation is essential to ensure credibility and fairness ( Kenny & Fluck, 2014 ). It is paramount that WIL programs are adequately resourced and staff supported ( Rook, 2017 ; Sachs et al., 2017 ; Wenham et al., 2019 ) if students, industry partners and universities are to reap the full potential of WIL to enhance student learning and employability in a sustainable way.

So too do universities need to consider and resource the best possible complement of academic and professional staff to support the sustainable development and delivery of WIL. Whitchurch (2008) ’s notion of a “third space” comprising “unbounded” or “blended” professionals provides a useful framework from which to unpack the different dimensions of WIL workload and design appropriate resource configurations to support it. WIL is an example of a space where professional and academic staff collaborate on projects of strategic importance ( Veles & Carter, 2016 ). It has been noted that WIL practitioners, both professional and academic, require broader (and sometimes different) skill sets than those required of classroom-based teachers ( Bates, 2011 ) and other University staff, for example interpersonal skills needed for partnership development and supporting students, and reflective capability to effectively teach reflective practice. These capabilities are not always adequately recognised in University frameworks for recruitment and remuneration ( Patrick et al., 2008 ; Wenham et al., 2019 ). Further there is blurring between the roles and responsibilities of academics and professional staff in this space, for example the high administrative load for course convenors ( Bates, 2011 ; Barrett & Barrett, 2008 ), and the important role professional staff play in supporting student pastoral care and learning. As Veles & Carter (2016) observe, the space in which these dynamic teams of “blended” professionals operate: “requires convergence of talent, fusion of skills and creative tension, [and] contributions from diverse personnel unencumbered by titles, job roles and position descriptions” (p. 522).

In addition to recruitment and remuneration practices, there is a need for universities to rethink the current staffing configurations which WIL is delivered and supported in higher education: are they fit for purpose? Doing so is not only vital for the just recognition of the contributions of the staff involved, but also to the success and sustainability of universities’ engagement agendas.

Throughout this paper we have alluded to areas where further research is required, including to enable a better understanding of the full range of drivers of variability in WIL workload across different modes of delivery. There is also benefit in conducting comparative empirical studies across institutions, disciplines and diverse WIL models to obtain a comprehensive overview of the impact of WIL on academic staff workload. Such research would provide additional data on the drivers identified in our study and potentially identify other contributors to workload, including student-related drivers. This is particularly timely considering the recent transition of many WIL activities to online delivery modes in response to the Covid-19 pandemic ( Zegwaard et al., 2020 ). Amassing a rigorous evidence base on these issues is important both to furthering our understanding of WIL workload, as well as to informing institutional and government decision-making on how best to resource the sustainable delivery of this valuable pedagogical strategy for student learning and employability development.

Partial financial support for conducting the study was received from a Macquarie University Research Enhancement Grant ($AUD25,000 awarded in 2015).

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

All authors whose names appear on the submission:

1) Made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data;

2) Drafted the work or revised it critically for important intellectual content;

3) Approved the version to be published;

4) Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Received from Macquarie University Human Research Ethics Committee Ref. No. 5201200467.

All participants in the study provided informed consent in accordance with the study-specific protocols approved by the Macquarie University Human Research Ethics Committee.