Measuring Collective Cognition in Online Collaboration Venues

Author(s):  
Paul Dwyer

By monitoring online conversations, organizations can receive value from the intellectual activity of their most interested constituents as they engage in problem solving and ideation. However, since intergroup dynamics often hinders people from optimizing collaboration, it should be measured and monitored for quality. Current metrics assess collaborative value solely from the number of collaborators, assuming that differences between individuals can be ignored. This study found that assumption to be wrong by identifying three distinct collaborator segments that strongly differ in the timing of their participation and in the variety of ideas they introduce. Therefore, a new metric is proposed that takes into account the diverse value individuals add. This new measure is correlated with existing measures only in those infrequent situations when collaboration productivity is maximized.

2011 ◽  
Vol 7 (1) ◽  
pp. 47-61 ◽  
Author(s):  
Paul Dwyer

By monitoring online conversations, organizations can receive value from the intellectual activity of their most interested constituents as they engage in problem solving and ideation. However, since intergroup dynamics often hinders people from optimizing collaboration, it should be measured and monitored for quality. Current metrics assess collaborative value solely from the number of collaborators, assuming that differences between individuals can be ignored. This study found that assumption to be wrong by identifying three distinct collaborator segments that strongly differ in the timing of their participation and in the variety of ideas they introduce. Therefore, a new metric is proposed that takes into account the diverse value individuals add. This new measure is correlated with existing measures only in those infrequent situations when collaboration productivity is maximized.


2005 ◽  
Vol 12 (3) ◽  
pp. 129-135
Author(s):  
Edward A. Silver ◽  
Jinfa Cai

Posing problems is an intellectual activity that is crucially important in mathematics research and scientific investigation. Indeed, some have argued that problem posing, as a part of scientific or mathematical inquiry, is usually at least as important as problem solving (Einstein and Infeld 1938; Hadamard 1945).


Author(s):  
Ke Zhang ◽  
Kyle Peck

Abstract. This study investigated the relative benefits of peer-controlled and moderated online collaboration during group problem solving. Thirty-five self-selected groups of four or five students were randomly assigned to the two conditions, which used the same online collaborative tool to solve twelve problem scenarios in an undergraduate statistics course. A score for the correctness of the solutions and a reasoning score were analyzed. A survey was administered to reveal differences in students' related attitudes. Three conclusions were reached: 1. Groups assigned to moderated forums displayed significantly higher reasoning scores than those in the peer-controlled condition, but the moderation did not affect correctness of solutions. 2. Students in the moderated forums reported being more likely to choose to use an optional online forum for future collaborations. 3. Students who reported having no difficulty during collaboration reported being more likely to choose to use an optional online forum in the future.


2009 ◽  
Vol 15 (6) ◽  
pp. 354-362
Author(s):  
Randall E. Groth ◽  
Claudia R. Burgess

Online conversations help teachers engage in constructive criticism and attend more carefully to aligning lesson plans with problem solving.


2020 ◽  
Author(s):  
Andy E Williams

The hypothesis that human intelligence represents a phase transition in animal intelligence is explored, as is the hypothesis that General Collective Intelligence (GCI), which has been defined as a system that organizes groups into a single collective cognition with the potential for vastly greater general problem-solving ability than that of any individual in the group, represents a phase transition in human intelligence. At these phase transitions, cognition can be demonstrated to gain the capacity for exponentially greater general problem-solving ability. If valid, then when generalized as an Nth order pattern, these N phase transitions represent successively more powerful super-intelligences, where each of these super-intelligences can potentially be implemented as an Artificial General Intelligence (AGI), or as a General Collective Intelligence (GCI).


Author(s):  
Holly Henry ◽  
David H. Jonassen ◽  
Robert A. Winholtz ◽  
Sanjeev K. Khanna

Problem solving is the primary intellectual activity of mechanical engineers. Therefore, enhancing problem-solving skills is essential for preparing mechanical engineering students for the workplace. The most powerful method for enhancing problem-solving skills is problem-based learning (PBL). This paper presents the design and construction of a PBL-based course in materials science at the junior level. We examine the ability of the course based on problems to enable students to learn both fundamental knowledge of the subject matter and also problem solving skills and contrast it with outcomes in a traditional lecture based course. The issues and challenges faced and qualitative evidence is presented.


2020 ◽  
Author(s):  
Andy E Williams

General Collective Intelligence has been defined as a system that combines individuals into a single collective cognition with the potential for vastly greater intelligence than any individual in the group [1], [2]. A novel Human Centric Functional Modeling approach [3] has been used define a model for this collective cognition, and for individual cognition [4], as well as for the intelligence of those systems of cognition, in order to quantify this potential increase in intelligence as exponential. Where other approaches assume the functions of cognition are implemented through mechanisms that are not yet confirmed, these functional models are defined from first principles and simply reflect all observed functionality rather than assuming any implementation at all. Here we show that from the perspective of these functional models, the transition from animal intelligence to a human intelligence capable of a sufficient level of abstraction to develop science and other concepts, and capable of exchanging and accumulating the value of those abstractions to achieve exponentially greater impact on the external world, is a well-defined phase change [5]. The transition from human intelligence to GCI, the transition from GCI to second order GCI, and so forth to Nth order GCI are hypothesized to be subsequent phase changes that may or may not occur [5]. The functional modeling approach is used to clarify the fundamentally different nature of the general problem-solving ability provided by GCI as opposed to the problem solving ability of tools such as computation or computing methods [6] that can be applied to any general problem, and why even super computers without general problem-solving ability are limited to the problems their designers can define, and to the solutions those designers can envision [7]. This model suggests that entire categories of problems cannot reliably be solved without this phase change to General Collective Intelligence, and since this exponential increase in problem-solving ability applies to physics, mathematics, economics, health care, sustainable development, and every other field of human study where intelligence applies. In addition, since this model suggests that any exponential increase in ability to impact the external world possible through GCI cannot have been possible before at any time in human civilization, and since another such increase cannot be possible again until the advent of AGI or the transition to a second order GCI. the implications of GCI are profound [8].


2021 ◽  
Author(s):  
Andy E Williams

A functional modeling approach is used to derive the properties that must be possessed by a platform with the capacity to significantly increase the general collective intelligence or c factor of groups. Such platforms have been termed “General Collective Intelligence” or GCI platforms. Having general problem-solving ability, a GCI potentially enables groups to execute any collective reasoning process, including abstracting (generalizing) a reasoning process so it might be reused in any other domain where it applies. A GCI can be shown to have the potential to exponentially increase the capacity of a group to create generalizations and other relationships, and capacity to store and exchange those relationships. Since relationships are concepts, and since the number of relationships between concepts better specify the location of any concept in conceptual space and therefore increases the density of conceptual space as a whole, GCI represents a phase change in collective cognition at which the collective conceptual space can expand exponentially in size and density. Each reasoning process connecting this far larger space of concepts has outcomes, making it potentially possible through these additional concepts to accumulate far greater impact on any outcome in the world. Because this phase change is not believed to have been possible at any point before in history, and is believed cannot occur again until the advent of another system with general problem-solving ability, such as a second order GCI or an Artificial General Intelligence (AGI), and because both AGI and second order GCI are believed to require GCI, GCI is proposed here to be the most important innovation in the history and immediate future of human civilization.


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