Designing Effective Work-Integrated Learning for Data Science Students: Considerations and Lessons Learned


  • Antonette Shibani University of Technology Sydney
  • Adrian Buck



WIL, Work integrated learning, data science, employability, higher education


Work integrated learning (WIL) equips students with real-world experiences by bridging the worlds of academia and work, theory and practice, reflection and action. Through WIL, students are able to gain critical life skills such as teamwork, problem-solving, communication, and professionalism, which are key for employability (Freudenberg et al., 2011; Jackson, 2015). They also get to apply the knowledge gained from classroom learning to practical projects, preparing themselves for the workforce. With the increasing demand for job-ready graduates, WIL remains a strategic priority for many higher education providers calling for significant investment in curriculum design (Billett, 2014).

Along with the noted benefits, the challenges and factors that require careful consideration for successful WIL design are well established (Jackson, 2015). Past studies have examined approaches and best practices for effective WIL, with more recent work focusing on technology and connectivity in a post-pandemic era (Dean & Campbell, 2020; Schuster & Glavas, 2017). In particular, the foundation lies in strong partnerships between higher education and industry, which is responsive to the needs of all stakeholders (Choy & Delahaye, 2011; Smith & Betts, 2000).

In our presentation, we discuss the considerations and lessons learned by educators in delivering a WIL subject for data science students in an Australian higher educational institution. Here, the underlying technicality of the subject involving data sharing and access, confidentiality, and intellectual property rights adds to contextual challenges. Additional challenges include the need to design a valuable student experience which can be scaled up for larger cohorts. The design of project-based learning experiences requires domain expertise, personalized mentorship, and pre-defined outcomes, prompting the shift towards a more problem-based approach where outcome spaces can evolve for transdisciplinary learning (Kligyte et al., 2021).

We derive insights from designing an interdisciplinary space for WIL projects with particular focus on the perspectives of different stakeholders involved and managing their expectations and boundaries. This involves co-defining shared problem spaces for projects to create a common understanding across all stakeholders (Billett, 2015); to inform more appropriately aligned authentic assessments (Ajjawi et al., 2020) that are embedded in project work; guiding, supporting and managing team formation and group dynamics to empower students to work as autonomous groups (Brewer et al., 2020); and the use of relevant tools and platforms to facilitate operations and aid learning (Schuster & Glavas, 2017). Feedback from students and industry partners has improved significantly across a 3 year period, with the current subject offerings receiving the highest student and industry satisfaction scores to date.

The work contributes to the design of effective WIL by learning from practical experiences of educators before, during, and following the pivot to online learning due to the COVID-19 pandemic, with lessons transferrable to other WIL contexts. Future work will build on the findings by triangulating with additional data sources including student work.






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