WORK INTEGRATED LEARNING IN DATA SCIENCE AND A PROPOSED ASSESSMENT FRAMEWORK
DOI:
https://doi.org/10.52041/serj.v21i2.26Keywords:
Statistics education research, Data science education, Work integrated learning, Authentic problem-based learning, Assessment frameworkAbstract
Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering; however, it has not been implemented until recently in statistics and not for every student in computer science education. There seems to be no literature on the use of WIL for data science education. With the changed focus of universities to making graduates “job ready”, university-industry collaboration widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. This shift in the curriculum, however, brought its challenges both for the students and their lecturers. In this paper, we present a case study and propose an assessment framework for data science WIL.
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