TY - JOUR
T1 - Anthropological thinking in data science education
T2 - Thinking within context
AU - Binah-Pollak, Avital
AU - Hazzan, Orit
AU - Mike, Koby
AU - Hacohen, Ronit Lis
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2024
Y1 - 2024
N2 - The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses reveal that students tend to neglect ethical concerns in their data analysis. Based on the data we collected from questionnaires distributed to undergraduate science and engineering students, this paper expands the discussion beyond human concerns and ethics in data science education. As we will show, students tend to neglect the context when attempting to solve data science questions. We argue that gaps in understanding the context relating to the data result in gaps in the analysis as well as in the interpretation of the data. Thus, we propose anthropological thinking as a pedagogy to overcome the context neglect. Placing the spotlight on the context promotes a holistic understanding of the phenomenon being analyzed, as it includes important considerations that do not necessarily fit the more commonly used term human concerns.
AB - The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses reveal that students tend to neglect ethical concerns in their data analysis. Based on the data we collected from questionnaires distributed to undergraduate science and engineering students, this paper expands the discussion beyond human concerns and ethics in data science education. As we will show, students tend to neglect the context when attempting to solve data science questions. We argue that gaps in understanding the context relating to the data result in gaps in the analysis as well as in the interpretation of the data. Thus, we propose anthropological thinking as a pedagogy to overcome the context neglect. Placing the spotlight on the context promotes a holistic understanding of the phenomenon being analyzed, as it includes important considerations that do not necessarily fit the more commonly used term human concerns.
KW - Anthropology
KW - Application domain
KW - Context
KW - Data science education
KW - Data thinking
UR - http://www.scopus.com/inward/record.url?scp=85181488422&partnerID=8YFLogxK
U2 - 10.1007/s10639-023-12444-7
DO - 10.1007/s10639-023-12444-7
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AN - SCOPUS:85181488422
SN - 1360-2357
JO - Education and Information Technologies
JF - Education and Information Technologies
ER -