Anthropological thinking in data science education: Thinking within context

Avital Binah-Pollak, Orit Hazzan, Koby Mike, Ronit Lis Hacohen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalEducation and Information Technologies
StateAccepted/In press - 2024


  • Anthropology
  • Application domain
  • Context
  • Data science education
  • Data thinking

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences


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