This blog is part of my reviewing of Seymour Papert’s Mindstorms (1980).
The challenge Papert sets himself is understanding why, in ‘rational’ cultures thinking is imagined as impeding actions, and even learning (95). To examine this view, he draws on arguments about ‘computer cultures’; ‘mathophobia’ as symptomatic of learning in dissociated ways and on the benefits of learning computational thinking through Turtle Geometry.
Referencing J. S. Bruner, Papert’s offers what he believes is a more a more flexible way of dealing with the apparent dichotomy of ‘thinking’ and ‘action’(96) which, he sees related to the categorical division we bring to things that are verbalizable OR nonverbalizable. Papert locates in the role of the ‘computer scientist’ a means of reconciling domains of knowledge, a potential he alludes to throughout Mindstorms as part of the transformational change which, he argues, the new technologies potentially bring to education (4, 38-39, 47).
Focusing on learning the physical skills of ‘juggling’, he considers ‘the development of descriptive languages for talking about learning’. The ‘right’ programming language makes visible ‘even to children the fact that learning a physical skill (generally characterised in education as nonintellectual) has much in common with building a scientific theory’ (96).
A key consideration for Papert is the way that programming languages give children access to mathematical formalist concepts and operations. In the juggling example, he shows how, words for the seventh-grader Robert, enable him to access formal concepts through ‘mind-size bites’. Indeed, the LOGO programming language gives him access to the idea of a ‘structured programme’(102) with ‘subprocedures’ that can ‘build an elaborate intellectual system without ever taking a step that cannot be comprehended (103).
Papert’s question of whether learning cascade juggling is helped or hindered by a verbal, analytic description of how to do it (106) is explored, not in terms of whether simulating the process on a computer is ‘right’ or ‘wrong’. Instead, how well can the programming language give rise to a computational model that can help construct ‘people procedures’ (106).
The last third of the chapter is devoted to explaining the application of modelling ‘people procedures’ in computer programming to a teaching strategy. Surprisingly, Papert reveals that after extensive research of LOGO, researchers confirm how the descriptive language of the program simply ‘facilitates debugging’ (111). Perhaps even more revealing is how Papert concludes that ‘computational procedures do not magically eliminate all repetitive processes from learning or that ‘the time needed to learn juggling can be reduced to almost nothing (113).
However, it does support more collaborative ways of working between teachers and students by, ironically, enabling students to see their teachers learning (114). It also sets up an urgency for using language in a multi-layered way: at the nomination of detail, the metalanguage of programme structure and the metaphorical organisation of same/different concepts. It is the sense of completeness which arises from languages for computers and people which Papert explores as ‘micro-worlds’ that ‘touch on fundamental issues of scientific method’ (117). Thus he concludes, the ‘internal intelligibility of computer worlds offers children the opportunity to carry out projects of greater complexity that is usually possible in the physical world (118).