Much of the difficulty – or the fun challenges – presented by the program of the open source learning laboratory is derived from the need to establish space and relationships for 4 design disciplines and 3 clusters of 150 people. [it helps in this regard to consult earlier blog posts on the reasoning for the existence of these disciplines, these clusters, and the number of people involved] I evaluated these characteristics of the laboratory in different ways, individually, based on spatial, temporal, programmatic, and sensual necessities/correlations.
In regard to relationships, given the basic scale of the program, of 450 students in 4 disciplines and 3 cluster, students are hierarchically organized in a large groups of 450 – the laboratory, smaller groups of 150 – the cluster, even smaller groups of 50-150 – the design area, and from there subdivided into smaller spaces based on need, time of day, and what they’re studying/learning/working on. In this way, the students’ spatial needs directly correlate to their learning needs and overarching categorization.
The design disciplines – industrial, media, culinary, and organizational design – are all inherently similar. While there are certain facility specialties and obvious occupational/socioeconomic differences, much of what they do is fundamentally the same. So it’s fair to say that given their similarity but specialty, they exist together on a spectrum of indefinite boundary conditions, commingling and coalescing, shifting over one another and mixing, among other synonyms.
As design programs, their similarity lies in the process, the ways of thinking, and subsequently the spaces necessary to accommodate these; these are thinkering (thought, design, make, all seamless), presenting, and storage, where thinkering takes up the largest space, and storage the least (thinkering is the primary goal of the laboratory and takes up the most time, storage is least necessary and much of it can be online or away from the primary programmed areas).
Based on research into modes of 21st century learning, it’s clear that students need a variety of spaces at a variety of scales with a variety of boundary conditions to maximize their learning ability. These are needed by all students and by all disciplines, so these are distributed
Clusters are groups of 150 students who collectively produce identity and community. What dictates how these are formed socially, and how are these formed spatially? How do students identify with their cohort, learning from people of disciplines, while simultaneously indentifying with members of their discipline? How do I create a multidisciplinary environment where students associate with a diverse group of people? How do students identify themselves – what is the most important thing about them? What kinds of things and people might learners want to be in contact with in order to learn? The effort here is to create a stimulating, linked environment; a community of people who are grouped to optimize their growth, where the importance is placed on their learning and not on their date of birth (how grades are currently organized). A lot of this is still in process, but I propose that students are organized based on their learning abilities, which helps to dictate ways in which they communicate (and develop abilities of communication with less struggle) and the spatial environments that arise to facilitate this.
so there are networks within clusters based on shared learning ability (learning identity) and within design disciplines based on shared design interests (occupational/activity (not literally occupation) identity). Just as well there are networks that exist among students of different clusters based on other relevant interests, and the goal of the laboratory is to help to foster and introduce more of these connections.
Mentors are another important part of this equation. Mentors are treated the same as students, but are in smaller numbers and have more empirical knowledge. They are part of the same clusters as students based on learning type, linked to the same disciplines based on field of interest, and create other external networks with other mentors based on other interests.
Overall, I look at the learning network as a fabric or cloud of design disciplines – blended and parametric – and the clusters as nodes in space, the kind of infrastructure that supports and controls the cloud. within this cloud and among this structure various networks exist between students and mentors, and here, where they find similar interest, the cloud clears. Or something like that. Fundamental to this concept is that clusters are stable (set number of students, set kinds of genetic learning types), while the design disciplines are unstable (prone to change or disappear in the long term, and shift in size and boundary based on yearly (students’ interests) and daily (student activity) needs). So the clusters are figure, structural platforms and they exist and create the design disciplines, which is the space in between.