Exploring Factors Contributing to Graduate Outcomes: Using Career Registration Methodology (CRM) to Track Students’ Employability Activities, Career Readiness, and Graduate Outcomes

Abstract

Graduate outcomes are a key indicator of university performance, yet the progression of students in career preparation during university is ill-understood. The Career Registration Methodology (CRM) addresses this gap by tracking students’ career planning and participation in employability and professional experiences (EPEs) throughout their university enrolment. This research used CRM to monitor students’ employability development and career readiness, assessing their impact on graduate outcomes. By analysing longitudinal CRM data and Graduate Outcomes Survey (GOS) results from 1653 students, this study examined how EPEs and career readiness influenced full-time employment, job offers, the perceived value of qualifications, and perceptions of overqualification. Correlation and trend analyses revealed positive associations between career readiness over time and employment outcomes, with regression analyses identifying EPEs as the most significant factor. Practical implications of the findings highlight CRM’s value in aiding higher education institutions, especially Work Integrated Learning (WIL) and Career Development Learning (CDL) educators, to identify trends and tailor support whilst students are still in the university. As the first CRM study in Australia based on the first available cohort, the exploratory nature of this research is acknowledged along with recommendations to refine periodic, non-intrusive measurements such as CRM for enhanced validity and reliability.

1. Introduction

Graduate outcomes are crucial in assessing the success of Australian higher education. The Higher Education Support Amendment (Job-Ready Graduates and Supporting Regional and Remote Students) Act 2020 was recently enacted to introduce performance-based funding requirements aimed at producing employment-ready graduates [1]. This legislation seeks to reform Australian higher education by adopting a responsive and employment-focused funding model. Significant investments have been committed to enhancing student employability, with universities now being held increasingly accountable for demonstrating performance and continuous improvement. The National Priorities and Industry Linkage Fund (NPILF) exemplifies this approach, linking funding to specific benchmarks and metrics related to student employability [2].

Despite the importance of measuring and evidencing student employability during their time at university, there has been a lack of formal, systematic measures to track how students progressively develop employability and prepare for professional careers. In Australia, the official cross-institutional measurement of student employability outcomes is the Graduate Outcomes Survey (GOS), conducted four months post degree completion [3]. However, between the time students enter and leave university, there is no official or consistent mechanism to observe students’ progression in career and employability development. This gap significantly impacts universities’ ability to monitor student progression and provide timely support. Consequently, areas within universities responsible for scaffolding employability are constrained in advancing the employability agenda [4,5]. Work Integrated Learning (WIL) and Career Development Learning (CDL) are two such key areas in need of timely ‘employability intelligence’ to tailor learning designs and facilitation. WIL focuses on using employability and professional experiences (EPEs) as part of formal learning [6], while CDL supports students in capitalising on EPEs to plan and prepare for navigating the world of work [7]. Without timely measurements during students’ formative years, educators and employability program facilitators are not only left with an incomplete understanding of the effects of WIL and CDL initiatives but also missed opportunities to scaffold and refine programs and interventions.

The Career Registration Methodology (CRM) was introduced over a decade ago to remedy the lack of ongoing, systematic measurements of students’ employability development and career readiness during the student lifecycle [8,9,10]. CRM emerged in the UK in 2012 to incorporate a short survey into the student enrolment processes. It is now used widely by higher education institutions in the UK and more recently in Ireland, Portugal, New Zealand, and Australia [11]. Universities embed the survey into the student enrolment cycles to obtain longitudinal data on students’ career planning and participation in EPE activities. The methodology was not adopted or piloted in Australia until 2019, with formal, systematic data collection beginning in 2020 at some universities. To date, over 70% of Australian universities have adopted or are in the process of incorporating CRM [12]. CRM provides a regular mechanism to track students’ EPEs and career planning, offering near real-time data on students’ progression statuses. This enables educators and career service providers to tailor program designs or allocate resources more responsively to meet the needs of diverse student cohorts.

Although CRM is gaining popularity as a brief, simple, periodic measure incorporated into enrolments and re-enrolments, few studies have been published on CRM [13]. The most recent large-scale study, conducted by Cobb [8], analysed data from 89,000 students in the UK. The study found that students who demonstrated higher career readiness in their final year (i.e., those in the ‘compete’ category) were more likely to secure employment, particularly graduate-level positions, shortly after graduation. Additionally, students who had work experience during their studies were more likely to find graduate employment compared to those without such experience [8]. However, further research is needed to strengthen the evidence base for CRM to justify large-scale institutional implementation in Australia and potential cross-institutional benchmarking in the future.

This study investigates the use of CRM in tracking students’ career development. Specifically, our objective is to link the CRM data collected before degree completion with the GOS data collected afterwards to explore the relationships between employability activities, career readiness (indicated by career planning activities), and graduate outcomes. Our primary research questions are (1) what is the relationship between EPEs, career readiness statuses, and GOS outcomes? and (2) what factors, as captured in CRM and the GOS, contribute to graduate outcomes? Additionally, considering CRM’s advantage of reducing the burden on respondents and the administration system, we also ask (3) do underlying themes exist in the activities or outcomes measured in CRM and the GOS? Identifying similar activities enables item reduction for conceptual clarity and survey efficiency [14].

In the following sections, we present an overview of the literature on employability and career-related measurements during the formative years of the student lifecycle, followed by related WIL and CDL literature with the rationale for the use of CRM. Next, we describe our quantitative research approach, utilising CRM and GOS data from the first cohort in Australia with available CRM and GOS results. Our analyses illustrate the relationship between EPEs, career readiness statuses, and graduate outcomes, whilst identifying significant contributors to these outcomes. We discuss our findings that align with previous studies but also reveal some anomalies. Given the exploratory nature of this study, based on the first available cohort in Australia with CRM and GOS results, we recommend refining CRM and propose ways to enhance its validity and reliability as directions for future research.

2. Literature Review

2.1. Employability and Career-Related Measurements During the University Student Lifecycle

Graduate employability and career development are multidimensional constructs [15,16,17]. They encompass the cognitive and functional knowledge and skills required for career management and the accumulation of significant social, cultural, psychological, identity, and human capitals [17,18,19]. Internationally, indicators of graduate employability are linked to high-level policies and strategies, with data reported through platforms such as CEDEFOP, OECD Education GPS, and ASEAN Work Plan Education 2021–2025 [20,21,22]. Findings from many studies contribute to holistic approaches for developing graduate employability and careers, involving intentional design of graduate capital and capacity building [5,18,23,24,25]. The literature on preparing students for professional careers commonly uses terms such as employability, work readiness, and career readiness. Each of these terms may have multiple definitions that lend themselves to diverse measurements [26]. The plurality and multidimensionality of these terms reflect the complexity of the concepts and their intersections [27]. While the nuanced contextual and conceptual differences between these terms are beyond the scope of this study, we adopt the approach of Bennett [28] as the basis to explore factors conducive to graduate outcomes in the higher education context. Bennett defines employability as ‘the ability to find, create and sustain meaningful work across the career lifespan and in multiple contexts’ (p. iv) [16].

In Australia, the ultimate measurement of employability is the national GOS, which provides a summative overview of graduates’ achievements based on immediate study and work outcomes four to six months after degree completion [3]. Recent research has specifically found strong labour market outcomes for undergraduate students who had undertaken work-based WIL [29]. However, the GOS collects WIL and employability-related data retrospectively as a concluding measure and does not capture the developmental aspect of employability during students’ time at university. Other measures that include developmental aspects of employability should be considered due to the dynamic process of employability. Many long-standing career theories and recent research emphasise factors influencing such development, referencing factors such as vocational interests and identities [30,31], life roles and stages [32,33], gender and occupational stereotypes [34,35,36], chance events and coping [37,38], and contextual factors and resources from social and political systems that are largely outside individuals’ control [39,40,41]. These theories and research highlight employability more as a process than a result.

Several measures of employability may be applied during the student lifecycles. The EmployABILITY Scale has 14 sub-scales covering factors ranging from self-awareness, interactive skills, technological and digital literacy, and emotional intelligence to ethical and responsible behaviours (134 items) [42]. The Australian Graduate Employability Scale (AGRADES) features dispositional employability, career futures, and job search self-efficacy (27 items) [43]. A measure of Responsibility of the University in Employability (RUE) incorporates institutional characteristics and efforts into the measure with four sub-scales (12 items) [44]. Some other measures focus on employability and its association with specific constructs such as innovation (25 items) [45], specific cohorts such as millennials [46], and internal and external factors (11 items) [47]. However, an over-reliance on subjective indicators in previous studies has been criticised in recent reviews of measures of employability. There is also concern regarding the lack of emphasis on specific actions that individuals can take to enhance employability [26,48].

In addition to the above, long measures reduce the scalability of the administration and can induce survey fatigue, lessening response accuracy. For example, related work readiness scales consist of 167 items for college students [49], while another work readiness scale contains 53 items incorporating employers’ perspectives [50]. In summary, many approaches identify employability as skills, abilities, or a multi-dimensional construct involving knowledge, skills, attributes, dispositions, and happenstance [26]. While these measures begin to form a comprehensive coverage of aspects of employability, a measure involving less subjectivity and more scalability will be required for implementing an ongoing systematic measurement during the student lifecycle.

2.2. Rationale for Career Registration Methodology (CRM)

The behavioural and developmental aspects of student employability are vital for WIL and CDL, the two most significant areas of higher education aimed at enhancing employability. WIL and CDL are both grounded in experiential learning cycles that emphasise practical experience, observation, reflection, abstraction, and informed experimentation [51]. WIL encompasses various work-related learning activities, such as placements, industry projects, fieldwork, and mentoring [6]. CDL focuses on using these experiences to continuously develop strategies for managing careers and life [52]. WIL and CDL are inextricably linked through work-based exploration that informs students’ understanding of themselves in the world of work. Through WIL, students gain practical career-related experiences, while CDL helps them integrate these experiences to form new career knowledge for life-long explorations. Understanding the behavioural patterns of students’ employability development is essential for WIL and CDL educators to provide effective and timely support.

CRM provides an annual snapshot of students’ engagement in EPEs and career planning [8]. The EPE question asks about the types of activities students have undertaken, whether through the curriculum or extracurricular means, such as work, internships, placements, extra-curricular activities, or volunteering. The career readiness question prompts students to choose a statement that best describes their career planning activity in the past year, which corresponds to one of four readiness statuses—decide, plan, compete, and sorted—with increasing levels of maturity. It is important to capture both EPE and career readiness. As noted by Daubney [53], a student may demonstrate high employability but low career readiness, meaning they may secure jobs while lacking direction in career paths. Since career planning refers to a process involving exploring, evaluating, goal-setting, strategising, and achieving [54], capturing these activities helps to understand students’ career thinking, decision making, and transition skills development, thereby indicating career readiness [7].

Tracking student trajectories through CRM reveals shifts in students’ perspectives. Research shows that students’ career interests and decidedness change over time. Quinlan and Corbin [55] found that 61% of students changed their career interests between the start of their studies and graduation and only 13% reported the same level of decidedness throughout that time. These changes align with the notion of learning gains, which refers to behavioural and conceptual changes in the development of skills, competencies, content knowledge and personal growth [56,57]. CRM allows a phased evaluation of these learning gains throughout a student’s journey in higher education [56].

One of CRM’s key advantages is its simplicity, as it integrates the questions into the regular student enrolment process with minimal burden on students. The two CRM questions, given their brevity, help avoid survey fatigue, a common issue in higher education [58]. Reducing survey fatigue increases response rates and accuracy [59]. Furthermore, unlike ad hoc measurements prone to sampling bias, when CRM is implemented as part of the regular, mandatory student enrolment process, it can avoid non-random sampling and self-selection bias, ensuring more consistent and representative data [60].

3. Materials and Methods

This study adopted a quantitative design to explore the relationship between students’ EPEs, career planning statuses, and selected graduate outcomes. Ethics approval was obtained from RMIT University Human Research Ethics Committee (Reference no. 26969).

3.1. Participants

Data were collected from 1653 undergraduate students who had responded to both CRM and GOS from an Australian University, an early adopter of CRM. The participants comprised 894 females (54.1%) and 758 males (45.9%) across 16 study areas, with 944 domestic students (57.1%) and 709 international students (42.9%).

Appendix A provides a detailed list of the participants characteristics which were covariates captured in CRM and GOS. These include gender, age group, Indigenous status, residency status, English-speaking background, low socio-economic status, first in family to attend university, study area, career readiness statuses, EPEs, and the number of job applications submitted in the final year of study or since graduating. Due to the small number of responses in the Indigenous and study area categories, these two covariates are excluded from further statistical analysis.

Appendix B provides details of the participants’ graduate outcomes. Approximately 43.3% of participants secured full-time employment within four months of completing their degree, while 67.3% attained overall employment status. The definition of full-time and overall employment is provided in the following section of measures. Additional outcomes include the number of job offers received during the final year of study or since graduating, the perceived value of their course or similar qualifications, and perceptions of overqualification.

3.2. Measures

EPEs and career readiness statuses were obtained via CRM, which consists of two questions (Appendix C). Students were asked to select all applicable EPEs from a list of 13 statements reflecting their activities in the previous 12 months. The other question required students to select one out of ten statements corresponding to one of four career readiness statuses, which were not disclosed to respondents: Decide (Statement 1–3), Plan (Statement 4–7), Compete (Statement 8 and 9), and Sorted (Statement 10). These statements were ordered randomly for respondents to select.

Graduate outcomes data were extracted from GOS, which was part of the national census within the Quality Indicators for Learning and Teaching (QILT) suite [3] (Appendix B). Graduates complete the survey four to six months after completing their degree. Participants responded to questions about their study or labour market engagement, including outcomes of full-time and overall employment. According to QILT, full-time employment is defined as being usually or actually in paid employment for at least 35 h per week in the week before the survey. Overall employment includes full-time, part-time, or casual employment. Other GOS outcomes include the perceived value of course or similar qualifications, and the perception of overqualification. The GOS also included an item about participation in Academic WIL and additional questions that institutions can opt into. These questions were provided by the Australian Association of Graduate Employers (AAGE) and the Australian Collaborative Education Network (ACEN). The AAGE questions asked for the number of job applications submitted and job offers received in the final year of the course and since graduating. The ACEN questions inquired about EPEs, including WIL and co-curricular activities.

Demographic information was collected via the university’s student administration management system. This included birth year, gender, first-in-family to attend university status, socioeconomic status, residency status, and Indigenous status.

3.3. Procedures/Data Collection

Data were sourced from the online enrolment system of an Australian university, an early CRM adopter, based on the first student cohort (2020–2022) participating in both CRM and the GOS. At this university, students were required to answer the two compulsory questions on EPE and career planning once a year to complete the online enrolment process. Students’ demographic information, including gender, study area, socioeconomic status, Indigenous indicators, and residency status, was extracted from the university’s student administrative system by the university’s data and analytics unit. This unit also matched the CRM data with the GOS data completed by this cohort after their degree completion. After de-identification, the data were analysed independently by the researchers of this study.

3.4. Data Analysis

The full list of covariates and outcomes is outlined in Appendix A and Appendix B. Due to small sample sizes, Indigenous status and study area were not included in the analyses. Although English-speaking background and the status of being first-in-family to attend university were initially included, they were excluded during model selection due to insignificance. Consequently, the covariates included in the final analysis were age groups (<22, 22–24, and over 24), gender, socioeconomic status, residency status (international/domestic), EPEs and career readiness statuses in the final-year CRM survey (Time 3), EPEs in the GOS survey, and job applications submitted in the final year and since graduating.

The graduate outcomes were full-time and overall employment, job offers received in the final year and since graduating, the perceived value of qualifications, and the perception of overqualification.

Factor analyses of EPEs captured in CRM and the GOS, as well as GOS graduate outcomes, were conducted [3]. Principal Component Analysis (PCA) using varimax rotation was applied to investigate potential factors in GOS perception of overqualification based on the similarity of response items [61,62].

Correlation and trend analyses were performed to study (1) the trend of career readiness statuses across data collection points in three enrolment years, (2) the relationship between EPEs and career readiness statuses, and (3) the relationship between career readiness statuses and graduate outcomes of full-time and overall employment.

Regression analyses were conducted to identify the relationship between covariates and graduate outcomes, including full-time employment, employment offers received in the final year and since graduating, perceived value of qualifications, and perception of overqualification. R [63] was used to perform the regression analyses. When the outcome variable was binary, logistic regression was performed. For ordinal outcome variables, ordinal regression was performed using the R package ‘ordinal’ [64]. Poisson regression was used for count outcome variable, such as the number of job offers received.

4. Results

4.1. What Is the Relationship Between EPEs, Career Readiness Statuses, and GOS Outcomes?

Correlation analyses found a highly significant (p < 0.001) low positive relationship between career readiness statuses at Times 1, 2, and 3 (

Table 1. Correlations between career readiness statuses at Times 1–3, full-time employment, and overall employment.

			Career Readiness Time 1	Career Readiness Time 2	Career Readiness Time 3	Full-Time Employment
Spearman’s rho	Career readiness Time 2	Correlation Coefficient	0.257
		Sig. (2-tailed)	<0.001 ***
		N	1013
	Career readiness Time 3	Correlation Coefficient	0.263	0.343
		Sig. (2-tailed)	<0.001 ***	<0.001 ***
		N	1005	1250
	Full-time employment	Correlation Coefficient	0.080	0.112	0.215
		Sig. (2-tailed)	0.011	<0.001 ***	<0.001 ***
		N	1014	1258	1262
	Overall employment	Correlation Coefficient Sig. (2-tailed) N	0.064	0.121	0.201	0.609
			0.041	<0.001 ***	<0.001 ***	<0.001 ***
			1014	1258	1262	1653

*** p < 0.001.

). There was a highly significant (p < 0.001) moderate correlation between GOS full-time employment and overall employment. Career readiness statuses at Time 2 and 3 showed a highly significant, low positive correlation with both GOS full-time employment and overall employment outcomes. No significant correlation was found between EPEs and career readiness statuses.

The trend analyses found that the trend of full-time employment mirrored that of overall employment based on career readiness statuses. Figure 1 illustrates the trend of full-time employment outcomes across Times 1–3. An increasing number of respondents reported being in the ‘compete’ and ‘sorted’ statuses at Times 2 and 3. Most respondents in full-time employment were in the ‘sorted’ category at Time 3. Significant upward linear trends in full-time employment, based on career readiness statuses, are evident across Times 1–3.

4.2. What Factors, as Captured in CRM and GOS, Contribute to Graduate Outcomes?

The regression analyses of covariates’ effects on different graduate outcomes found that the only two outcomes with no significant factors were overall employment captured in the GOS and the perception of how well the degree prepared the respondent for their job (

Table 2. Significant factors of graduate outcomes—employment outcomes ^{† ‡}.

Covariates	Estimate	SE	z Value	p Value
Full-time employment †
Age group under 22	−1.02	0.27	−3.75	<0.001 ***
Age group over 24	−0.66	0.31	−2.17	0.030 *
Residency: International	−0.60	0.30	−2.04	0.042 *
GOS Academic WIL	−0.58	0.28	−2.11	0.035 *
GOS Paid work whilst studying: Part-time not relevant to intended career	−0.57	0.29	−1.98	0.048 *
GOS Paid work whilst studying: None	−0.83	0.37	−2.63	0.024 *
Employment offers in the final year and since graduating ‡
Age group under 22	−0.45	0.16	−2.80	0.005 **
Age group over 24	−0.42	0.20	−2.06	0.039 *
Low Socio-Economic Status	−0.87	0.35	−2.47	0.014 *
CRM Full-time work	1.43	0.50	2.85	0.004 *
CRM Global Experience	1.21	0.26	4.61	<0.001 ***
GOS As part of course: not work-based WIL	−0.52	0.18	−2.88	<0.001 ***
GOS Not academic WIL: A position of responsibility in club/society	0.52	0.18	2.83	0.005 **
GOS Not academic WIL: being a mentee	1.11	0.20	5.59	<0.001 ***
GOS Not academic WIL: Co-curricular leadership or award program	−1.36	0.34	−3.98	<0.001 ***
GOS Not academic WIL: Not-for-academic credit: Micro-credentialing or digital badge program	0.89	0.19	4.62	<0.001 ***
GOS Not academic WIL: Other	0.87	0.35	2.46	0.014 *
GOS Not academic WIL: None	0.56	0.20	2.86	0.004 **
GOS Paid work whilst studying: Part-time not relevant to intended career	−0.74	0.16	−4.59	<0.001 ***
GOS Paid work whilst studying: Other	0.96	0.28	3.42	<0.001 ***
GOS Paid work whilst studying: None	−0.64	0.22	−2.97	0.003 **

*** p < 0.001, ** p < 0.01, * p < 0.05; ^† Logistic regression, ^‡ Poisson regression.

).

Overall, very few demographic factors were significant, except for age group, which was significant for eight out of the thirteen outcomes. Compared with the reference group aged 22–24, both the younger cohort (under 22) and older cohort (over 24) had fewer full-time employment outcomes and employment offers since their final year of study or graduation. Both cohorts also perceived themselves as having more job skills and education than required. Additionally, the younger cohort rated higher on the necessity of their qualification for their current job but lower on the importance of their qualifications. They also perceived their current job as requiring less education and believed that someone with less education could perform their job well.

Gender was not a significant factor in any of the outcomes. Low socio-economic status had a negative association with the number of employment offers in the final year or since graduating. International students had fewer full-time employment outcomes compared with domestic students. They also rated higher than domestic students in the perception that their current job required less education.

Apart from the demographic factors of being younger or older than 22–24 and coming from low socio-economic backgrounds, several factors were associated with fewer job offers during the final year and since graduating. These included participating in non-work-based WIL as part of their course, co-curricular leadership or award programs, part-time paid work unrelated to intended careers, and not having any paid work whilst studying. Conversely, several activities were associated with more employment offers, including full-time work, global WIL experience, student society positions, mentee experience, micro-credentialing or digital badge program participation, and other non-academic WIL or paid work activities.

Table 3. Significant factors of graduate outcomes—perceived value of qualification ^†.

Covariates	Estimate	SE	z Value	p Value
Qualification required for current job
Age group under 22	0.99	0.27	3.62	<0.001 ***
Age group over 24	0.32	0.31	1.05	0.29
CRM Work placement during program	−0.95	0.40	−2.38	0.017 *
CRM Full-time work alongside studies	1.83	0.84	2.18	0.029 *
CRM Position of responsibility in student group/society	−1.47	0.57	−2.56	0.011 *
GOS Not-for-academic-credit: volunteering	0.62	0.27	2.28	0.023 *
GOS Paid work whilst studying: Full-time relevant to intended career	−1.27	0.57	−2.23	0.026 *
GOS Paid work whilst studying: Part-time relevant to intended career	−1.11	0.29	−3.87	<0.001 ***
Qualification being important for current job
Age group under 22	−0.83	0.23	−3.71	<0.001 ***
Age group over 24	−0.18	0.25	−0.73	0.47
CRM Paid work whilst studying: Part-time related to program	0.68	0.25	2.76	0.006 **
CRM Fulltime work alongside studies	−1.62	0.59	−2.75	0.006 **
CRM No experience to date	−0.62	0.27	−2.26	0.024 *
GOS As part of course: Workplace-based WIL	0.68	0.24	2.76	0.006 **
GOS As part of course: Global WIL experience	−1.15	0.54	−2.15	0.031 *
Course providing sufficient engagement with industry professionals
CRM As part of course: Work placement	0.62	0.24	2.60	0.009 **
GOS Applications submitted in final year and since graduating	−0.01	0.002	−2.10	0.036 *

*** p < 0.001, ** p < 0.01, * p < 0.05; ^† Ordinal regression.

reports significant results for the perceived values of qualifications. Several questions evaluate the perceived value of qualifications, including whether the qualifications were perceived as being required, important, or providing sufficient engagement with industry professionals. Regarding the qualification being required, respondents who were under 22, did full-time work alongside studies, or those who did volunteering rated positively on their qualifications being required for their current job. In contrast, those who did work placement during their programs of study or held positions in student groups or societies rated negatively on this item. Undertaking full-time or part-time paid work relevant to intended careers was associated negatively with the perception that the qualification was required for the current job, according to the GOS responses. However, full-time work alongside studies as measured by CRM had a positive association with the perception that the qualification was required for current work.

On the question of the qualification being important for current work, those who did part-time work related to their programs of study and those who did work-based WIL perceived their qualifications as being important for their current work. The covariates of being under 22, having full-time work, no EPE to date, and having undertaken global WIL experience shared a negative association with perceiving the qualification as being important.

Regarding the qualification providing sufficient engagement with industry professionals, students who did work placement as part of their course reported through CRM that their course provided sufficient engagement with industry professionals. However, the number of applications submitted in the final year and since graduating had a negative association with the perception of their course providing sufficient engagement with industry professionals.

Table 4. Significant factors of graduate outcomes—perceptions of overqualification ^†.

Covariates	Estimate	SE	z Value	p Value
Current job requires less education
Age group under 22	0.71	0.23	3.09	0.002 **
Age group over 24	0.28	0.26	1.10	0.273
Residency: International	0.65	0.25	2.64	0.008 **
GOS As part of course: Work placement	−0.54	0.20	−2.77	0.006 **
GOS Paid work whilst studying: Full-time relevant to intended career	−0.81	0.36	−2.26	0.024 *
GOS Paid work whilst studying: Part-time relevant to intended career	−0.79	0.22	−3.61	<0.001 ***
Respondent has more job skills than required
Age group under 22	0.63	0.22	2.82	0.005 **
Age group over 24	0.50	0.25	2.00	0.046 *
CRM Global experience less than one year	1.88	0.81	2.33	0.020 *
GOS Paid work whilst studying: Part-time relevant to intended career	−0.72	0.21	−3.40	<0.001 ***
Someone with less education could perform job well
Age group under 22	0.68	0.22	3.12	0.002 **
Age group over 24	0.11	0.25	0.44	0.657
GOS As part of course: Work placement	−0.45	0.19	−2.38	0.017 *
GOS Not-for-academic-credit: Enterprise incubator/start-up	2.39	0.88	2.72	0.007 **
Respondent’s previous training is fully utilised
CRM Full-time work prior to starting program	1.10	0.45	2.43	0.015 *
GOS As part of course: Work placement	0.58	0.24	2.41	0.016 *
GOS Not-for-academic credit: Mentee in an industry-based mentoring arrangement	1.30	0.42	3.11	0.002 **
GOS Not-for-academic credit: Other	−1.60	0.68	−2.36	0.018 *
GOS Paid work whilst studying: Other	−1.39	0.53	−2.60	0.009 **
GOS Paid work whilst studying: None	−0.66	0.27	−2.43	0.015 *
Respondent has more knowledge than needed
GOS Paid work whilst studying: Part-time relevant to intended career	−0.72	0.21	−3.50	<0.001 ***
Respondent’s education level above the level required
Age group under 22	0.62	0.23	2.70	0.007 **
Age group over 24	0.53	0.25	2.09	0.036 *
CRM Industry project related to program	1.44	0.48	3.03	0.002 **
CRM Career Readiness Status: Compete	−0.62	0.30	−2.08	0.038 *
GOS Not-for-academic-credit: volunteering	0.50	0.21	2.33	0.020 *
GOS Not-for-academic credit: Other	−1.60	0.65	−2.49	0.013 *
GOS Paid work whilst studying: part-time relevant to intended career	−0.73	0.22	−3.34	<0.001 ***
Someone with less work experience could do just as well
CRM Full-time work prior to starting program	−1.12	0.48	−2.36	0.018 *
GOS As part of course: Work placement	−0.41	0.19	−2.09	0.037 *
GOS Not-for-academic credit: Enterprise incubator/start-up	1.68	0.84	2.01	0.045 *
GOS Paid work whilst studying: Part-time relevant to intended career	−0.64	0.22	−2.95	0.003 **
Respondent has more abilities than needed
GOS Not-for-academic credit: Volunteering	0.61	0.22	2.81	0.005 **
GOS Paid work whilst studying: Full-time relevant to intended career	−0.77	0.35	−2.18	0.030 *
GOS Paid work whilst studying: Part-time relevant to intended career	−0.79	0.21	−3.74	<0.001 ***
GOS Applications submitted in final year and since graduating	0.004	0.002	2.01	0.045 *

*** p < 0.001, ** p < 0.01, * p < 0.05; ^† Logistic regression.

presents significant results for perceptions of overqualifications. Eight items measured if respondents perceived themselves as being overqualified. Most respondents with EPEs disagreed. Respondents who had work placement or full-time or part-time work relevant to intended careers, work placement as part of their course, or other EPEs tended to disagree that their work required less education, job skills, knowledge, or abilities. Respondents who had work placements as part of their course were less likely to agree that someone with less education could perform their job equally well. Additionally, having full-time work experience before starting their program, participating in work placements during their course, or engaging in part-time work relevant to their intended career were factors that negatively influenced their belief that someone with less work experience could perform as effectively. Respondents with experiences in global WIL, enterprise incubators or start-ups, industry projects related to their program, volunteering, or more job applications rated positively on their perception that they were underutilised.

Regarding previous training being fully utilised, those who had full-time work before starting their program of study, had work placement as part of their course, or had been a mentee responded positively. However, those who did not have paid work whilst studying rated negatively on this item.

A wide range of EPEs as reported through CRM and the GOS were significant factors in the diverse graduate outcomes. However, only one career readiness status was significant in its negative association with the perception of overqualification. Respondents with the ‘compete’ status tended to disagree that their education level was above the level required for their work.

4.3. Do Underlying Themes Exist in the Activities or Outcomes Measured in CRM and GOS?

The factor analyses conducted on career planning statuses, EPEs in CRM and the GOS, and most GOS graduate outcomes yielded inconclusive results. The explained variance in these analyses was relatively low, ranging from 45% to 55%, while the number of factors identified remained high. Additionally, the factors lacked conceptual coherence, often being too similar to each other, making interpretation and labelling challenging. The perception of overqualification items were found to be highly similar through a Principal Component Analysis (PCA) (

Table 5. GOS perception of overqualification component matrix.

Item	Component
Respondent has more job skills than are required	0.999
Someone with less education than respondent could perform job well	0.999
Someone with less work experience than respondent could do just as well	0.999
Respondent has more abilities than needed	0.998
Job requires less education than respondent has	0.995
Respondent’s education level is above the level required	0.995
Respondent’s previous training is being fully utilised	0.995
Respondent has more knowledge than needed	0.994

). The top five factors, each with an eigenvalue greater than 1, accounted for 99.95% of the variance.

5. Discussion

The findings shed light on CRM as a methodology, EPEs and career planning statuses, and diverse graduate outcomes.

5.1. Relationship Between Career Planning Statuses, EPEs, and GOS Outcomes

The finding of significant, low positive correlations between career planning statuses across data collections is consistent with the study by Quinlan and Corbin [55] that changes in career thinking do not remain fixed. Fluctuations are expected. In our case, we could see variation in the state of students’ career planning from year to year, reflecting the dynamic nature of career development and the potential influence of diverse factors [32,41,65].

No specific EPEs correlate with career planning statuses, suggesting that employability experiences and the state of career planning do not have an exclusive or direct relationship. This should not be taken as a sign of EPEs being irrelevant to career planning; on the contrary, EPEs may be used at any stage of career planning depending on their needs, circumstances, and purposes. For example, a work placement can be used by someone in the ‘decide’ stage, just as volunteering can be undertaken by someone in the ‘compete’ stage. There may not be a direct or exclusive relationship between activities and phases of career planning. A more nuanced understanding of career planning and EPEs is required to observe how they interact to make a difference.

There is a positive correlation between career planning statuses and GOS employment outcomes across data collections. Linear upward trends of employment (full-time and overall) based on the four career planning statuses exist across three years of data collection, with more people with the ‘compete’ and ‘sorted’ statuses in the second and final data collections. As time moves on, the correlation between the more advanced statuses and employment outcomes grows. In Year 2, a slight, noticeable downward trend between the compete and sorted statuses may be observed. This trend likely reflects the Dunning–Kruger effect [66]. As students gain more experience and maturity in career exploration, some might take a step back to review and modify their plans. As a result, some students may have stayed at or returned to the compete phase. By Year 3, more students may move on to the sorted phase, indicating a weakening of this stagnation. However, despite the above, only the ‘compete’ career readiness status is found to have a direct effect on the perception of overqualification. Respondents in the compete state tend not to perceive their education level as being above what is required. Given the significant results of EPEs, it is likely that career planning activities and EPEs are confounded; therefore, their interactions may need to be studied further.

5.2. Contributing Factors to GOS Outcomes

A wide range of covariates are associated with graduate outcomes. Consistent with findings of recent studies [29], EPEs are the dominant factors influencing graduate outcomes. The main demographic item that stands out as a significant factor for graduate outcomes is age group. Those who complete their degrees at the age of 22–24, accounting for almost half of the respondents, report better full-time employment outcomes, receive more job offers, have a higher perception of the importance of their qualifications, and feel less overqualified. At first glance, one might think this finding reveals a Goldilocks’s ideal age of degree completion, not too early and not too late. However, this likely reflects the typical graduate recruitment market favouring recent graduates of a similar or typical age profile. Few, if any, other demographic factors in this study significantly influence the diverse graduate outcomes. Gender is not a significant factor for any of the outcomes, nor is the variable of English-speaking background being the first in the family to attend university. Low socio-economic status has only a negative association with the number of employment offers in the final year. Compared with domestic students, international students’ residency status also is only significant in having a negative association with full-time employment and perceiving having more education than their job required. Being more qualified for their jobs, especially in the initial stage of their careers in Australia, has been a common reality shared not only by international students but also recent migrants [67].

The outcome of employment offers in the final year of courses and since graduating is the outcome with the most influencing factors, most of which being EPE factors and half of them were highly significant (p ≤ 0.001 ***). Over half of the factors identified have a positive association with increased employment offers. However, this study does not include factors outside of the individual, such as labour market conditions. Many more factors beyond what have been identified in this research could exist.

Overall, the most significant type of factor in the diverse graduate outcomes is EPEs identified by regression analyses. In contrast, only one career readiness status is significant; namely, the career planning status of ‘compete’. It has a negative relationship with the perception of overqualification at the education level, meaning respondents at the compete stage are less likely to view their education level as above what is required. This may suggest that respondents at the compete stage may be more realistic about work options. They may have been applying for jobs at suitable levels such as entry-level or graduate jobs. They may also have been informed by employers’ responses to their applications and adjusted their job search strategies.

The perceived value of qualifications may have been impacted by graduates’ overall university experience. Well-rounded graduates may have less reliance on the perceived value of the qualifications in the job market. Several factors may have provided the participants with reasons to perceive their qualifications as being less required for their current job. These include positions of responsibility in a student group/society, full-time and part-time work relevant to intended careers, and even work placement during the program of study. Similarly, respondents with full-time work alongside studies or global WIL experience perceive qualifications as less important for their current job.

Some factors may decrease the perception of overqualification. Respondents with work placement as part of their course or full-time/part-time work relevant to intended careers tend to disagree that their current jobs require less education. Respondents with part-time work relevant to their intended careers may be less likely to think that they have more skills than required. Those with work placement as part of their course may disagree that someone with less education could perform the job just as well. Having full-time work prior to starting a program, work placement as part of the course, or not-for-academic-credit mentee experience could be conducive to the perception of adequate utilisation of previous training. Part-time paid work relevant to the intended careers may reduce the perception that respondents have more knowledge than needed. Being in the compete stage of career planning and having part-time paid work relevant to intended careers may reduce the perception of education level being above what is required. Respondents with full-time work before starting their program of study, work placement as part of the course, or part-time work relevant to intended careers may be less likely to think that someone with less work experience could do just as well. Having full-time or part-time paid work relevant to intended careers may also reduce the perception that the respondent possess more abilities than needed.

In contrast to the above experiences lessening the view of overqualification, the lack of EPEs is significant in perceiving qualifications as not important for current job. The lack of paid work experience is also significant in viewing previous training as not being fully utilised, whilst decreasing full-time employment outcomes.

5.3. Reducibility of EPE, Career Planning Status, and GOS Outcome Items Included in This Study

For the most part, there are no further underlying themes in the career planning activities, EPEs as measured by CRM or the GOS, or GOS outcomes included in this study. These items are distinctively different. However, the eight questions about overqualification could be merged into one, due to the items’ high similarity, to achieve greater survey economy.

5.4. Unexpected Results

This study contains several unexpected results that require further research to explore the nuances of the findings. Academic WIL and part-time paid work not related to intended careers have a slight negative effect on full-time employment. This is inconsistent with the finding from a recent large-scale study that work-based WIL benefits graduates with bachelor degrees [29]. Non-work-based WIL, co-curricular (non-WIL) leadership or award programs, and part-time work not related to intended careers are negatively associated with employment offers in the final year and since graduating. However, the number of employment offers do not necessarily represent the competitiveness of a graduate. After all, one only needs to secure a desired job offer to conclude their job search. On the other hand, graduates who experience more challenges in securing suitable work may end up receiving more job offers due to a prolonged job search. This is reflected in the finding of this study that those who submit more job applications disagree that their course provided sufficient engagement with industry professionals.

The lack of significant results based on career readiness statuses may suggest issues with the selection of statements. Currently, although multiple statements may apply to a respondent, only one can be selected, which might inaccurately represent a respondent’s career readiness. Additionally, the way statements correspond to career readiness statuses is arbitrary and open to debate. Confounding factors may also affect participants selecting statements within the same career readiness status. For instance, participants choosing statements in the ‘plan’ category, such as ‘planning to apply for further study’ and ‘planning to start my own business’, may have very different traits that directly influence graduate outcomes.

6. Conclusions

Employability development and career preparation are complex phenomena. The emergence of CRM presents an opportunity to collect student EPEs and career planning data periodically through enrolments. This study adds to evidence-based practice in WIL and CDL by analysing CRM and GOS data to identify factors contributing to graduate outcomes. A wide range of EPEs have been found to be significant.

By investigating the relationship between the CRM and GOS data, this research demonstrates the value of incorporating measurements of EPEs and career readiness, such as CRM, in the student lifecycle. The periodic, non-intrusive measurement can provide essential data for WIL and CDL practitioners to identify trends and tailor support at the cohort and program levels. By focusing on the developmental and behavioural aspects of employability, a measure like CRM can also complement concluding measures of employability such as the GOS.

We note several contributions of this research. Theoretically, we fill a gap in the literature of employability measurement by highlighting the developmental and behavioural aspects of career development. Methodologically, we explore and demonstrate the use of CRM, based on the concept of learning gain, as a way to track employability development through activities and behaviours. As a method gaining momentum since its origin in the UK and now being trialled in around 70% of Australian universities [12], this research is timely to provide evidence for its utility and value in gauging employability development. We used the first available cohorts’ CRM and GOS data to explore the relationship between EPEs, career readiness, and graduate outcomes. Practically, we examined data reducibility in the survey. Based on this sample, we detected a potential reduction of the GOS eight overqualification items into one in probing overqualification. Overall, we demonstrate that a brief measurement method such as CRM, embedded in the regular part of the student enrolment system, may provide valuable insights for WIL and CDL educators.

Several significant and unexpected findings may prompt educators to rethink factors influencing graduate outcomes. Overall, age group is a significant variable across diverse outcomes. In contrast, gender is not significant, while residency and low socio-economic status have significance in only three outcomes. By far, the EPEs identified through CRM and the GOS are the most influential across all factors. EPEs may operate non-exclusively with career planning activities and may be used for multiple career planning phases. Further investigation may clarify whether their interactions can affect graduate outcomes.

As this is the first analysis of CRM and GOS data in Australia based on available data, we stress the exploratory nature of this research. We note that this sample does not fully represent all student cohorts due to the three-year timeframe. Some degrees and double degrees take more than three years. We also note that the students in this study were impacted by COVID-19, during which significant challenges and disruptions barred students from fully participating in employability-building activities. Furthermore, while CRM is compulsory at the university where the data was collected, GOS participation is not. The GOS contains many voluntary questions. Consequently, missing data in the GOS presents challenges for the statistical analysis because it is uncertain if the assumption of randomness of missing data can be met absolutely.

We observe a lack of significant results regarding career readiness statuses influencing graduate outcomes, prompting questions about statement selection, confounding factors, and the alignment between statements and career readiness statuses. These issues suggest the need to refine the CRM for improved validity and reliability. Further studies are necessary to compare results when respondents can select all applicable career readiness statements rather than being limited to one. Additionally, re-evaluating the correspondence between each career readiness statement and status is important, especially because higher education institutions can self-determine the alignment between career readiness statements and statuses. This means future cross-institutional comparisons need to ensure consistency of the statement-status alignment. Identifying confounding factors that may affect the classification of career readiness statuses is also essential.

Another technical consideration is that EPEs measured in CRM and the GOS may appear to be similar, although not identical. Differences in EPE wording in CRM and the GOS hinder direct comparison. We note discrepancies may exist between the EPE responses in CRM and the GOS. The CRM’s EPE descriptions are less specific about whether activities are part of an academic program (WIL) (Appendix C). Rather, the descriptions address more broadly whether the activities are program-related. In contrast, the ACEN questions included in the GOS separate for-academic-credit activities, not-for-academic-credit activities, and paid work. The timing of the data collection of CRM and the GOS may also result in discrepancies between EPE responses due to participant recollection. To sum up, CRM may gauge how students prepare for professional careers, but practical and philosophical limitations concerning CRM as a methodology need to be carefully considered [24].

Future studies should focus on refining CRM as well as including cohorts that previously could not be meaningfully analysed due to low respondent numbers, such as Indigenous students. Additionally, research should address the challenge of collecting sensitive personal data, such as disability information, to better capture the experiences of all students. By fully including diverse student cohorts, further analyses can enhance the utility of CRM and support evidence-based practice.

Continuous data collection and further research are highly recommended. Although CRM is gaining traction globally, few studies exist to evidence its effectiveness. The study of Cobb [8] found that over 40% of undergraduate students remained in the ‘decide’ phase in their final year, indicating that more needs to be done to support students’ study to work transition. This transition, as indicated by theories of planned happenstance [37,41] and chaos [65], may be far more dynamic than is often assumed. Findings from this study suggest that higher education institutions should capitalise on EPEs’ benefits and consider career development learning interventions to maximise the benefits of EPEs. This may include sustaining opportunities for quality work-integrated learning and fostering stronger industry partnerships to provide students with practical experience and networking opportunities. Integrating employability and career development learning into the curriculum may create personal relevance in students’ academic journey over time. To assess the impact of WIL and CDL initiatives, it is crucial to monitor the development of career preparedness and provide timely support during students’ university years. Establishing an effective system to track students’ employability activities, career readiness, and graduate outcomes should be a priority for higher education policymakers and providers.