Deep learning : a case for graduate apprenticeships

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Faulkner-Jones, Alan John and du Plessis Love, Odin and Ilyas, Adnan and Zahid, Adnan and Westacott, Robin; (2022) Deep learning : a case for graduate apprenticeships. In: Proceedings of the 8th International Symposium for Engineering Education. University of Strathclyde, Glasgow. ISBN 9781914241208

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Abstract

Work-based learning is driven by the need to learn to do a job. For Graduate Apprenticeships (GAs), which lead to a degree and therefore degree-level roles, the structure of GA programmes needs to be more flexible and the assessment more contextualised in order for apprentices at this level to meet the wide range of needs of graduate employers and vice versa. The expectation is that success in the workplace through learning to do a job, perform a role, undertake a project etc. is driven by deep learning – the need to understand the how and why – rather than the surface learning that is part of the learn, pass, forget cycle that many learners fall into in modular programmes. Graduate Apprentices can learn in the traditional way, but also from other apprentices and other colleagues, and these forms of learning promote thinking and reflection. Traditional academic programmes deliver the same teaching and learning to every learner at the same stage of the programme and assess each learner in the same way, commonly using formal examinations as well as coursework. With work-based learning, because every job role is different, there is the opportunity to provide unique learning and assessment opportunities for each apprentice within the same degree framework. To make work-based learning degrees work, the assessment needs to be made up of activities undertaken in the workplace. Unlike the traditional assessments, these GA assessments won’t be rigid but will be individually tailored to each apprentice based on both course and workplace requirements. This paper discusses how deep learning is embedded in Heriot-Watt University’s Graduate Apprenticeships programmes.

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https://doi.org/10.17868/strath.00082052
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