scholarly journals Beyond IQ: The Importance of Metacognition for the Promotion of Global Wellbeing

2021 ◽  
Vol 9 (4) ◽  
pp. 54
Author(s):  
Lav R. Varshney ◽  
Aron K. Barbey

Global policy makers increasingly adopt subjective wellbeing as a framework within which to measure and address human development challenges, including policies to mitigate consequential societal problems. In this review, we take a systems-level perspective to assemble evidence from studies of wellbeing, of collective intelligence, and of metacognition and argue for a virtuous cycle for health promotion in which the increased collective intelligence of groups: (1) enhances the ability of such groups to address consequential societal problems; (2) promotes the wellbeing of societies and the individual wellbeing of people within groups; and, finally, (3) enables prosocial actions that further promote collective problem-solving and global wellbeing. Notably, evidence demonstrates that effective collaboration and teamwork largely depend on social skills for metacognitive awareness—the capacity to evaluate and control our own mental processes in the service of social problem-solving. Yet, despite their importance, metacognitive skills may not be well-captured by measures of general intelligence. These skills have instead been the focus of decades of research in the psychology of human judgment and decision-making. This literature provides well-validated tests of metacognitive awareness and demonstrates that the capacity to use analysis and deliberation to evaluate intuitive responses is an important source of individual differences in decision-making. Research in network neuroscience further elucidates the topology and dynamics of brain networks that enable metacognitive awareness, providing key targets for intervention. As such, we further discuss emerging scientific interventions to enhance metacognitive skills (e.g., based on mindfulness meditation, and physical activity and aerobic fitness), and how such interventions may catalyze the virtuous cycle to improve collective intelligence, societal problem-solving, and global wellbeing.

2010 ◽  
Vol 41 (2) ◽  
pp. 112-123 ◽  
Author(s):  
Marianne Frauenknecht ◽  
David R. Black

2021 ◽  
Vol 35 (2-3) ◽  
pp. 209-214
Author(s):  
Frank Fischer

Abstract. This discussion first highlights novel aspects that the individual articles contribute to the special issue on (future) teachers' choice, use, and evaluation of (non-)scientific information sources about educational topics. Among these highlights are the conceptualizations of epistemic goals and the type of pedagogical task as moderators of the selection and use of scientific evidence. The second part raises overarching questions, including the following: How inclusive do we want the concept of evidence to be? How should teachers use research evidence in their pedagogical problem-solving and decision-making? To what extent is multidisciplinary teacher education contributing to epistemological confusion, possibly leading to (pre-service) teachers' low appreciation of educational research?


1982 ◽  
Vol 46 (2) ◽  
pp. 48-59 ◽  
Author(s):  
Donald R. Lehmann ◽  
William L. Moore ◽  
Terry Elrod

This paper examines Howard's (1963) typology dividing decision making into extensive problem solving (ESP), limited problem solving (LSP), and routinized response behavior (RRB). Specifically, the amount of information accessed in a longitudinal experiment is studied. Information acquisition is modeled stochastically at the individual level, and the existence of two segments (LSP and RRB) is tested in a nested-model framework.


BMC Nursing ◽  
2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Soleiman Ahmady ◽  
Sara Shahbazi

Abstract Background The complex health system and challenging patient care environment require experienced nurses, especially those with high cognitive skills such as problem-solving, decision- making and critical thinking. Therefore, this study investigated the impact of social problem-solving training on nursing students’ critical thinking and decision-making. Methods This study was quasi-experimental research and pre-test and post-test design and performed on 40 undergraduate/four-year students of nursing in Borujen Nursing School/Iran that was randomly divided into 2 groups; experimental (n = 20) and control (n = 20). Then, a social problem-solving course was held for the experimental group. A demographic questionnaire, social problem-solving inventory-revised, California critical thinking test, and decision-making questionnaire was used to collect the information. The reliability and validity of all of them were confirmed. Data analysis was performed using SPSS software and independent sampled T-test, paired T-test, square chi, and Pearson correlation coefficient. Results The finding indicated that the social problem-solving course positively affected the student’ social problem-solving and decision-making and critical thinking skills after the instructional course in the experimental group (P < 0.05), but this result was not observed in the control group (P > 0.05). Conclusions The results showed that structured social problem-solving training could improve cognitive problem-solving, critical thinking, and decision-making skills. Considering this result, nursing education should be presented using new strategies and creative and different ways from traditional education methods. Cognitive skills training should be integrated in the nursing curriculum. Therefore, training cognitive skills such as problem- solving to nursing students is recommended.


2003 ◽  
pp. 225-240
Author(s):  
Ray Webster

This chapter considers the use of cognitive styles and metacognitive skills in the design and development of e-learning environments. Participants involved in a unit in Human Computer Interaction used the results of a Riding’s Cognitive Styles Analysis to assist in the design and development of Web-based Individual Learning Environments (ILEs). Student reflections and cognitive styles results are considered in terms of their impact on the design process. They are also used to consider participants’ metacognitive awareness of their own cognitive and learning styles. It is suggested that the use of cognitive styles in this manner will produce interfaces and environments more suited to the learning requirements of each individual. In addition, the process of reflecting on and using the style results will help develop more metacognitively aware learners. The individual environment and metacognitive awareness are both desirable elements for a student-centered learning system for successfully participating in virtual education.


2006 ◽  
Vol 41 (2) ◽  
pp. 307-317 ◽  
Author(s):  
Osvaldo F. Morera ◽  
Albert Maydeu-Olivares ◽  
Thomas E. Nygren ◽  
Rebecca J. White ◽  
Norma P. Fernandez ◽  
...  

Author(s):  
XIAO-JUN YANG ◽  
LUAN ZENG ◽  
RAN ZHANG

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM.


2020 ◽  
Author(s):  
Andy E Williams

A model of cognition suggests that the left vs right political debate is unsolvable. However the same model also suggests that a form of collective cognition (General Collective Intelligence or GCI) can allow education, health care, or other government services to be customized to the individual, so that individuals can choose services anywhere along the spectrum from socialized services if they desire, or private services if they desire, thereby removing any political stalemate where it might prevent any progress. Whatever services groups of individuals choose, GCI can significantly increase the quality of outcomes achievable through either socialized or private services today, in part through using information regarding the fitness of any services deployed, to improve the fitness of all services that might be deployed. The emerging field of General Collective Intelligence (GCI) explores how platforms might increase the general problem-solving ability (intelligence) of groups so that it is significantly higher than that of any individual. Where Collective Intelligence (CI) must find the optimal solution to a problem or group of problems, having general problem-solving ability, a GCI must also have the capacity to find the optimal problem to solve. In the case of political discussions, GCI must have the ability to re-frame political discourse from being focused on questions that have not proved resolvable, such as whether or not left leaning or right leaning political opinions are in general more “right” or “wrong”. Instead GCI must have the ability to refocus discussions, including on how to objectively determine whether a left or right bias optimizes outcomes in a specific context, and why. This paper explores the conjecture that determining whether a left leaning or right leaning cognitive bias is "optimal" (i.e. "true) based on any CI or other aggregate of individual reasoning that is not GCI, cannot reliably converge on "truth" because each individual cognitive bias leads to evaluating truth according to different reasoning types (type 1 or type 2) that might give conflicting answers to the same problem. However, through using functional modeling to create the capacity to represent all possible reasoning processes, and through using functional modeling to represent the domains in conceptual space in which each reasoning process is optimal, it is possible to systematically categorize an unlimited number of collective reasoning processes and the contexts in which execution of those reasoning processes with a right leaning or left leaning bias is optimal for the group. By designing GCI algorithms to incorporate each bias in its optimal context, a GCI can allow individuals to participate in collective reasoning despite their biases, while collective reasoning might still converge on "truth" in terms of functioning to optimize collective outcomes. And by deploying intelligent agents incorporating some subset of AGI to interact on the individual's behalf at significantly higher speed and scale, collective reasoning might gain the capacity to consider all reasoning and all "facts" available to any individual in the group, in order to converge on that truth while significantly increasing outcomes.


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