TY - JOUR
T1 - Work integrated learning in data science and a proposed assessment framework
AU - Bilgin, Ayse Aysin Bombaci
AU - Powell, Angela
AU - Richards, Deborah
N1 - ©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.
PY - 2022/7/4
Y1 - 2022/7/4
N2 - 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.
AB - 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.
KW - Statistics education research
KW - Data science education
KW - Work integrated learning
KW - Authentic problem-based learning
KW - Assessment framework
UR - http://www.scopus.com/inward/record.url?scp=85135030284&partnerID=8YFLogxK
U2 - 10.52041/serj.v21i2.26
DO - 10.52041/serj.v21i2.26
M3 - Article
SN - 1570-1824
VL - 21
SP - 1
EP - 12
JO - Statistics Education Research Journal
JF - Statistics Education Research Journal
IS - 2
M1 - 12
ER -