The lower rates of science, technology, engineering and mathematics (STEM) academic performance by underserved groups (Black, Latinx, Native in the US) is a well-known problem. Some of the disparity is tied to long-standing effects of colonialism and racism. Stereotypes, myths of racial neurogenetics and formation of ‘oppositional identities’ have created self-fulfilling prophecies (Hoff & Walsh, 2017). Over the last two decades, our team has developed a suite of online tools focussed on increasing the interest and performance of underrepresented students. Culturally Situated Design Tools (CSDTs) [https://csdt.org/] are based on heritage algorithms: the maths and computing ideas that exist in cultural practices. By simulating the original designs, students learn the STEM knowledge of Indigenous peoples, street artists and more. By extending these practices with their own creative agency, and building bridges to local community development, these tools can help students see themselves as a part of transformative change. With the possibility of teaching moved online due to Covid-19, and the new attention to anti-racist curricula, CSDTs may be just the right combination for 2020.

The project began in 1994, with Eglash’s computational modelling of fractal structures in African design. The aerial photos of villages showed circles of circles of circles, rectangles within rectangles, often down to tiny scales. A year of fieldwork revealed that these scaling geometries were not restricted to architecture. There were fractals in textiles, carving, metalwork, beadwork, and so on. Far from mere decoration, the scaling patterns represented the recursive structures of Indigenous cosmology, kinship and life’s regenerative cycles. Readers pressed for time may want to skip the book (Eglash 1999) and watch the Ted Talk [https://www.ted.com/talks/ron_eglash_the_fractals_at_the_heart_of_african_designs?language=en].

One of the best examples in which CSDTs support these African heritage algorithms is our cornrow braiding simulation [https://csdt.org/culture/cornrowcurves/index.html]. Students first learn what cornrow patterns communicated in the original context; how they crossed the Atlantic in the slave trade; and how the tradition was reborn in the emancipatory resurgence of the civil rights movement and hiphop. They then use a scripting interface to learn the underlying principles: iterative loops of geometric transformations (scaling, rotation, translation, reflection) create braids; nested iteration creates braids of braids.

In emulation of the Indigenous commons, students can upload their finished product to our community site [https://csdt.org/projects/], and others can download their creation for further modification; we currently host over 17,000 user-created projects. In the final step students can physically render their designs – in the case of cornrows, that has involved 3D printers creating mannequin heads, which are then installed in local braiding shops. Adult braiders contributed new ideas for CSDTs such as our pH Empowered [https://csdt.org/culture/phempowered/index.html] tool for applying arduino-based sensors to beauty products; one student is now marketing her own organic, pH neutral shampoo. These programs show statistically significant improvement in controlled studies comparing students using CSDTs in comparison with comparable non-cultural software (for an overview see Eglash et al., 2020; more publications available on our website [https://csdt.org/publications/]).

Readers sometimes misinterpret what we are doing as ‘sugarcoating: tricking students into learning. But of course that is not the point. The reason European science looks so different from Indigenous science is not because it is superior. It was created for the purpose of extracting value: labour value from workers; ecological value from plantations; sucking social value out of our online networks. That is why top-down control – linearisation, optimisation, routinisation, and so on – has been such a fundamental theme. Deep engagement with Indigenous ways of knowing offers profound insights into other possibilities for maths and computing; for forms of STEM that were created for preventing value alienation rather than facilitating it.

In recent publications (Eglash et al., 2019, 2020) we sketch out what a generative economy would look like, and how STEM education would be incorporated. Some first steps appear on our website Generative Justice [https://generativejustice.org/projects/], where we provide open-source hardware and software to the adult community, such as this project for combining heritage algorithms, AI and digital fabrication to ‘artisanal cyborg’ production in Ghana. In the long term we hope that this synthesis of education and economic development can develop into an emancipatory ecosystem; one in which unalienated value can freely circulate between people, nature and society.

This blog is based on the article ‘Decolonizing posthumanism: Indigenous material agency in generative STEM’ by Ron Eglash, Audrey Bennett, William Babbitt, Michael Lachney, Martin Reinhardt and Deborah Hammond‐Sowah, published in the British Journal of Educational Technology.

References

Eglash, R. (1999). African fractals: Modern computing and Indigenous design. New Brunswick, NJ: Rutgers University Press 1999.

Eglash, R., Bennett, A., Babbitt, W., Lachney, M., Reinhardt, M., & Hammond‐Sowah, D. (2020). Decolonizing posthumanism: Indigenous material agency in generative STEM. British Journal of Educational Technology, 51(4), 1334–1353. https://doi.org/10.1111/bjet.12963

Eglash, R., Robert, L., Bennett, A., Robinson, K. P., Lachney, M., & Babbitt, W. (2019). Automation for the artisanal economy: Enhancing the economic and environmental sustainability of crafting professions with human–machine collaboration. AI & Society, 1–15. Retrieved from https://www.springerprofessional.de/en/automation-for-the-artisanal-economy-enhancing-the-economic-and-/17233726

Hoff, K., & Walsh, J. (2017). The whys of social exclusion: Insights from behavioral economics. World Bank Research Observer, 33(1), 1–33, https://doi.org/10.1093/wbro/lkx010

Acknowledgement: The authors would like to acknowledge National Science Foundation grants DRL-1640014 and DGE-0947980 in support of this work.