A WIL assessment meta-framework for discipline-specific employability learning
DOI:
https://doi.org/10.21153/jtlge2024vol15no1art1949Keywords:
work integrated learning (WIL), assessment framework, STEM, career, authenticAbstract
Work integrated learning (WIL), particularly placement-based WIL (P-WIL), has gained momentum in Higher Education over the last decade as an educational strategy for enhancing student employability. However, there is very limited guidance on effective ways to embed and scaffold WIL assessments across courses (degree programs). We present the findings from an action-research study, purposed with building academic confidence to review and re-design existing assessments to have intentional and explicit employability foci. Key to the study was the sharing of existing WIL frameworks to build practitioner expertise. What arose was a notable intersection of similar, but unplanned assessments focused on career development learning, authentic assessment, and/or demonstrations of theory-to-practice performance during WIL activities. What was lacking was a means for designing an intentional holistic schema that tagged a suite of assessments dedicated to the development and demonstration of student employability across the course. An outcome was the creation of a novel meta-assessment WIL framework (WAM-F) that supported and purposefully integrated previously independent items: 1) career development learning; 2) transferable skills development; 3) reflections from professional practice theory-to-practice WIL experiences; and explicitly tethered these to the discipline orientation of the course. The overt tethering of discipline-specific learning outcomes to a range of WIL activities, via a course-wide approach, not only assures the regulatory requirement for all WIL experiences to contribute to, scaffold and develop the learning outcomes of a course, but also makes sense of emerging educational approaches for STEM teaching teams not always familiar or confident with how to embed fit-for-purpose employability learning.
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