Colabo.Space is an ecosystem for a collective mind, where the notion of a collective mind represents the model of collectiveness, its interactions across, and improvements of a community.
Having various communities, practices, needs, community values, methodologies of work, cultures, levels of freedom and creativity, there is demand for different models or rather implementations of a collective mind. Therefore, Colabo.Space has to be generic, declarative and flexible.
To achieve that, we have evaluated numerous disciplines and research fields to build the infrastructure of a collective mind and principles which the system that is implementing a particular instance of it (for the specific community) should follow.
We designed and implemented ColaboFramework that represents the infrastructure for a collective mind based on the findings. It represents the core foundation of Colabo.Space.
Principles for healthy design and development of a collective mind we have articulated and assembled into Colabo.Space Manifesto (or collective mind manifest).
Computer-Supported Cooperative Work (CSCW)
A great deal of our work with Colabo.Space aligns with the Computer-Supported Cooperative Work (CSCW) research field. It seems that CSCW has two different roles (of our interest, among many) - 1) observatory role - understanding CSCW phenomena (referring to the type of work CSCW as a scientific field is interested in), and 2) guidance role CSCW and design of computer systems, guided with CSCW findings and foundation.
In our understanding, CSCW did not manage to govern the design and development of cyberinfrastructure, to introduce practices, policies, and heuristics that would support the healthy development of cyberinfrastructure, supporting both its infrastructure and organization needs, as well as the requirements of each actant (~ actor) in the system, and focus on reducing the socio-technical gap. Cyberinfrastructure is left at the mercy of non-domain IT experts, investors, businesses and communities that try to “reprogram” their patterns according to system capabilities and functionalities. In an adjacent field, Human-Computer Interaction (HCI) managed to establish more rigid mechanisms of evaluation and provided quick and dirty evaluation heuristics (Nielsen, 1994; Gerhardt‐Powals, 1996; Weinschenk & Barker, 2000).
Through our work on Colabo.Space, we are aiming to strengthen an unfulfilled (in our opinion) role of the CSCW, the guidance role and, in such a way, to promote positive system design patterns.
Activity Theory, Actor-Network Theory, BPMN and ColaboFlow
In our research, we drew a great deal of our inspiration from the CSCW "observatory role" research corpus; Activity Theory (AT) (Leont'ev, 1974), Actor-Network Theory (ANT) (Latour, B. 1996), dynamic theory of work (Bardram, 1998), boundary object (Star & Griesemer, 1989), awareness, etc.).
These flows are essential as they resolve the collective dissonance introduced in our work by placing the AT theory in parallel with the ANT theory and to the BPMN purpose. However our rationale is the following, on the one hand, through the AT theory, we put the community and its members in the context of their interests, goals and we support their freedom and creative process in making choices, preferences, expressiveness, way of realizing tasks and the outcomes of the tasks. On the other hand, we do put them in "equivalence" with machines, that are choosing, optimizing and suggesting interactions and "way-to-do." On top of that, we orchestrate or rather guide (by proposing, suggesting and insights) the overall process and interactions through flows (ColaboFlow as an extension of BPMN). Therefore this "coordinative" aspect can be instead recognized as a set of rules of the game that will increase the collective mind outcome of the overall process (flow).
Cognitive load theory
In our collective systems design and implementation, we have The Cognitive load theory (Sweller, 1988) introduces and recognizes three types of cognitive load:
- intrinsic cognitive load - the effort associated with a specific tasks demand/complexity,
- extraneous cognitive load - the effort associated with the process of resolving tasks (coming from the task performance methodology and tools) and
- germane cognitive load - the effort put into creating cognitive representations of acquired knowledge, or a schema
An intrinsic (cognitive) load is the most inherent and the hardest one to reduce due to its dependence on the working task and the nature of the problem that needs to be solved. However, there are many examples of transforming a problem into one that is easier to solve (for example, the transposition of an integral from the space of rational numbers into the space of complex numbers which makes some problems trivial). Similarly, redesigning and modularizing (Gerjets et al., 2004) (Gerjets et al., 2006) studying problems shows that even intrinsic cognitive load can be reduced.
In the case of designing collaborative systems for solving open problems, it is hard to integrate transformation and modularizing components that would generally be suitable for any open problem. Thus, to reduce intrinsic cognitive load, we need to design and provide a system with free knowledge space that different stakeholders can contribute to, as well as to articulate and modularize the problem space encoded through knowledge entities. This realization contributed to our design of KnAllEdge, a concept (and component) of knowledge space and a key aspect of our solution. System participants must also be able to continue expanding their knowledge with additional facts, findings and relationships. They should be able to freely transform and tweak knowledge as it increases in entropy and value, or transform it into more meaningful and easier-to-solve knowledge problems. Tackling this aspect of the intrinsic cognitive load was one of the reasons we propose the concept of the MindStuff, a set of transformative puzzles, tools that "sit" on top of the knowledge space and incrementally improve it.
- Bardram, J. (1998). Designing for the dynamics of cooperative work activities. DAIMI Report Series, 27(536).
- Gerhardt‐Powals, J. (1996). Cognitive engineering principles for enhancing human‐computer performance. International Journal of Human‐Computer Interaction, 8(2), 189-211.
- Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32(1-2), 33-58.
- Gerjets, P., Scheiter, K., & Catrambone, R. (2006). Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations?. Learning and Instruction, 16(2), 104-121.
- Latour, B. (1996). On actor-network theory: A few clarifications. Soziale welt, 369-381.
- Leont'ev, A. (1974). The problem of activity in psychology. Soviet Psychology 13(2):4–33.
- Leigh Star, S. (2010). This is not a boundary object: Reflections on the origin of a concept. Science, Technology, & Human Values, 35(5), 601-617.
- Nielsen, J. (1994, April). Usability inspection methods. In Conference companion on Human factors in computing systems (pp. 413-414). ACM.
- Star, S. L., & Griesemer, J. R. (1989). Institutional ecology,translations' and boundary objects: Amateurs and professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39. Social studies of science, 19(3), 387-420.
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
- Weinschenk, S., & Barker, D. T. (2000). Designing effective speech interfaces (Vol. 1). New York: Wiley.