Updated Sep 07, 2026
Low participation in an employability activity can have several explanations. Nicola Fish, Santina Bertone and Bernadine van Gramberg explore students’ accounts at one Australian university.
Thirty business students were interviewed: eight completing their first year and 22 in their final year. The case concerns students enrolled in 2017–2018 at a university near Melbourne and focuses on opportunities outside their course.
The authors identify emotional and behavioural barriers, including stress and feeling overwhelmed. Reported institutional issues included awareness, relevance and timing of opportunities, and their absence from class time. Students also described uncertainty about their professional futures.
The paper proposes connecting student engagement, employability and career-development learning more closely. It does not test whether a specific curriculum change improved employment outcomes, or establish that the same barriers will occur at another institution.
The publisher’s abstract and selected methods were accessible for this review. Detailed results and an earlier participant quotation could not be fully verified and have not been retained.
As editorial suggestions, examine how students learn about opportunities and whether they can realistically participate. Ask those who do not attend about their reasons, rather than interpreting absence as lack of interest.
Compare reported barriers with scheduling and participation information. A local pilot could test a clearer invitation, different timing or appropriate curriculum integration, with an agreed evaluation. Comments can inform that design; they do not independently prove causes or predict progression and employment.
Nicola Fish, Santina Bertone and Bernadine van Gramberg. “Improving student engagement in employability development: recognising and reducing affective and behavioural barriers.” Studies in Higher Education. DOI: 10.1080/03075079.2025.2461271.
Reviewed 7 September 2026 against accessible publisher sections. The revision adds the data period, removes an unverified quotation and narrows causal and predictive claims. This is a limited-access review.
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