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Within the larger arc of the Information Age, we have entered a time of Big Data. This is an era marked by businesses and politicians exploiting the ability to predict and influence our preferences based on structures and patterns gleaned from large-scale statistical analyses. Now, in new ways, each of us exists as an instance of a category. This is the latest phase of a movement rooted in the Age of Industry – the rise of abstraction, from the alienation and commodification of labor to the recasting of the human agent as a locus of correlated behaviors.
In this talk I will review an experiment in undergraduate education that centers hands-on weaving in a course focused on elucidating computer science approaches to the analysis of structures and patterns. This is an attempt to restore the things behind the signs, to tacitly reconnect complexity with labor, and to help students feel the reductive brutality twinned with the tremendous technical power of abstraction. To further develop the theme of material intelligence and its parallels with formal reasoning, the course considers resonances of indigenous and contemporary aesthetics in textile art, as well as relationships between coding theory and the design and notation of weaving drafts [1]. Mass production of textiles was a primary driver of the Industrial Revolution and its concomitant exploitations of colonized peoples, but looms and hand-weaving can play at least a small counterbalancing role in the classroom today. As noted by Rosner, Shorey, Craft, and Remick [2], “worlds of hand-work and computing … are not as separate as we might imagine them to be.”
In this course, students learn to read weaving drafts and thread an eight-shaft table loom. We then introduce ideas from computer science in ways that relate directly to students’ fresh experiences with weaving. For example, we consider the question of how to quantify the complexity of an image and relate this to the fact that some weaving patterns inherently require more shafts and longer pick sequences than others. This leads to the concept of algorithmic complexity, grounded in student experience using looms to make cloth, in contrast to conventional pedagogy based on hypothetical Von Neumann machines. The ideas are reinforced by considering digital images of woven structures of greater or lesser pattern complexity and noting that the sizes of the corresponding (jpeg) files vary accordingly. Similarly, after the students learn to follow “block” weaving drafts, natural openings arise to discuss key concepts of data compression. We consider ways in which weaving is both analog and digital, and compare the advantages of analog and digital modes of representing and expressing images.
Laboring to make cloth from yarn leads students to reassess consumer behaviors such as fast fashion – as Yuriko Saito puts it [3], they begin to experience “a more engaged interaction with the material world.” Classroom studies of the ancient origins of weaving likewise help them understand that technologies based on binary codes [1] are not an invention of the modern West. While this course stands alongside contemporary studies such as [4] and can benefit from assimilating research results at the intersection of education and computational craft, it seems unusual in its focus on computer science concepts as opposed to computer technology skills.
[1] E. Harlizius-Klück, Textile 15, 176 (2017). [2] D. K. Rosner et al., Proc. 2018 CHI Conf. on Human Factors in Computing Systems, 1 (2018). [3] Y. Saito, J. Aesthet. Art Crit. 76, 429 (2018). [4] V. Mirecki et al., Designing Interactive Systems Conference (DIS ’22).
