Work integrated learning in data science and a proposed assessment framework

Ayse Aysin Bombaci Bilgin; Angela Powell; Deborah Richards

doi:10.52041/serj.v21i2.26

Work integrated learning in data science and a proposed assessment framework

Ayse Aysin Bombaci Bilgin^*, Angela Powell, Deborah Richards

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

53 Downloads (Pure)

Abstract

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.

Original language	English
Article number	12
Pages (from-to)	1-12
Number of pages	12
Journal	Statistics Education Research Journal
Volume	21
Issue number	2
DOIs	https://doi.org/10.52041/serj.v21i2.26
Publication status	Published - 4 Jul 2022

Bibliographical note

©International Association for Statistical Education (IASE/ISI). First published in Statistics Education Research Journal, 21(2), Article 12. https://doi.org/10.52041/serj.v21i2.26. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Statistics education research
Data science education
Work integrated learning
Authentic problem-based learning
Assessment framework

Access to Document

10.52041/serj.v21i2.26

Publisher version (open access)Final published version, 260 KB

Cite this

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title = "Work integrated learning in data science and a proposed assessment framework",

abstract = "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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AU - Richards, Deborah

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KW - Authentic problem-based learning

KW - Assessment framework

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Work integrated learning in data science and a proposed assessment framework

Abstract

Bibliographical note

Keywords

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Industry collaboration through work-integrated learning in a capstone unit

Reflections on designing a statistical consulting capstone unit

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