Educational Science

Education in a digital world

We live in a world of algorithmic sorting and decision-making. Mathematical models are curating our social relationships, influencing our elections, and even deciding whether or not we should go to prison (Brennan, Dieterich & Ehret 2009, O’Neil 2016, Eubanks 2017). But how much do we really know about code, algorithmic infrastructures and their cultural implications? When truth can be embodied in texts, truth can be massaged through forgery or misrepresentation (see Stock 1987, 62). This also applies for algorithms, since they get it wrong sometimes and they are not always deterministic but rather ambigious, especially considering cultural implications and biased data (see Uricchio 2017, Wachter-Boettcher 2017).

This short term research project will give insights on how to understand programming as coding literacy in the context of educational science by incorporating latest research findings from software studies and critical code studies. This will be done in two steps: first it will be outlined, why it is essential that we need to learn to understand not only the functioning of code, but the way code signifies (see Marino 2020, 5). Secondly, the relation between programming and writing will be adjusted in the light of coding literacy, as established by Vee (2017). Accordingly, programming has a complex relationship with writing; it is writing, but its connection to the technology of code and computational devices and therefore the performativity of code itself also distinguishes it from writing in human languages at the same time.

The research will provide a theoretical and methodological insights on how to address the cultural implications of code and algorithmic infrastructures and how to assess code critically.

Involved partners

• Creative Technologies (ctech) at the Filmuniversity Konrad Wolf in Potsdam Babelsberg/Germany.
• Center for Teacher Education at the Otto-von-Guericke-Universtiy of Magdeburg

Duration

01.09.2019 - 30.06.2021

References

• Brennan, T., Dieterich, W., & Ehret, B. (2009). Evaluating the Predictive Validity of the Compas Risk and Needs Assessment System. Criminal Justice and Behavior, 36(1), 21–40. https://doi.org/10.1177/0093854808326545
• Eubanks, V. (2017). Automating inequality: How high-tech tools profile, police, and punish the poor (First Edition). St. Martin’s Press.
• Marino, M. C. (2020). Critical code studies: Initial methods. The MIT Press.
• O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy (First edition). Crown.
• Stock, B. (1987). The implications of literacy: Written language and models of interpretation in the eleventh and twelfth centuries (Repr. ed). Princeton Univ. Press.
• Uricchio, W. (2017). 8. Data, Culture and the Ambivalence of Algorithms. In M. T. Schäfer & K. van Es (Eds.), The Datafied Society (pp. 125–138). Amsterdam University Press. https://doi.org/10.1515/9789048531011-011
• Vee, A. (2017). Coding literacy: How computer programming is changing writing. The MIT Press.
• Wachter-Boettcher, S. (2017). Technically wrong: Sexist apps, biased algorithms, and other threats of toxic tech (First edition). W.W. Norton & Company, independent publishers since 1923.