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Optimal Experience and Optimal Identity: A Multinational Study of the Associations Between Flow and Social Identity.

ABSTRACT: Eudaimonistic identity theory posits a link between activity and identity, where a self-defining activity promotes the strength of a person's identity. An activity engaged in with high enjoyment, full involvement, and high concentration can facilitate the subjective experience of flow. In the present paper, we hypothesized in accordance with the theory of psychological selection that beyond the promotion of individual development and complexity at the personal level, the relationship between flow and identity at the social level is also positive through participation in self-defining activities. Three different samples (i.e., American, Chinese, and Spanish) filled in measures for flow and social identity, with reference to four previously self-reported activities, characterized by four different combinations of skills (low vs. high) and challenges (low vs. high). Findings indicated that flow was positively associated with social identity across each of the above samples, regardless of participants' gender and age. The results have implications for increasing social identity via participation in self-defining group activities that could facilitate flow.

SUBMITTER: Mao Y

PROVIDER: S-EPMC4760053 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Optimal Experience and Optimal Identity: A Multinational Study of the Associations Between Flow and Social Identity.

Mao Yanhui Y Roberts Scott S Pagliaro Stefano S Csikszentmihalyi Mihaly M Bonaiuto Marino M

Frontiers in psychology 20160219

Eudaimonistic identity theory posits a link between activity and identity, where a self-defining activity promotes the strength of a person's identity. An activity engaged in with high enjoyment, full involvement, and high concentration can facilitate the subjective experience of flow. In the present paper, we hypothesized in accordance with the theory of psychological selection that beyond the promotion of individual development and complexity at the personal level, the relationship between flo ...[more]

PMID: 26924995

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Project description:While the formulation of Mihaly Csikszentmihalyi's theory of flow, including the experience dimensions, has remained stable since its introduction in 1975, its dedicated measurement tools, research methodologies, and fields of application, have evolved considerably. Among these, education stands out as one of the most active. In recent years, researchers have examined flow in the context of other theoretical constructs such as motivation. The resulting work in the field of education has led to the development of a new model for understanding flow experience in education, specifically dedicated to adult learning. As a result of both a meticulous analysis of existing models and consideration of more recent developments, a new flow scale has thus been developed. The aim of this study is therefore twofold: to validate the new flow measurement scale dedicated to the educational environment, EduFlow-2, and to test a new theoretical model. Students taking a course (N = 6,596), some on-site and others in a MOOC, participated. Several scales were administered online at the end of the participants' course during the 2017 academic year. The factor structure of EduFlow-2 was tested using Exploratory Structural Equation Modeling. Several models were tested. The model with a second-order factor best fit the data. We tested the invariance of the flow scale measure for gender and for the type of training (MOOC/on-site). We were able to show that the flow scale is invariant of the modalities of these two variables. Results revealed good psychometric qualities for the scale, making it suitable for both on-site and distance learning. The analysis also revealed significant relationships with the classic variables of motivation, self-efficacy, learning climate, and life satisfaction. Furthermore, all four dimensions of the model were found to be adequate and consistent with the underlying theoretical arguments. In the end, this new, short flow scale and the theoretical model were demonstrated to be promising for future studies in the field of education.

Project description:ObjectiveThe objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum.Existing challengesDue to the nonconvexity of the problem, the search for a solution to OPF problems is not scalable, which makes the OPF highly limited for the system operation of large-scale real-world power grids-"the curse of dimensionality". The recent attempts at distributed computation aim for a scalable and efficient algorithm by reducing the computational cost per iteration in exchange of increased communication costs.MotivationA new network model allows for efficient computation without increasing communication costs. With the network model, recent advancements in distributed computation make it possible to develop an efficient and scalable algorithm suitable for large-scale OPF optimizations.MethodsWe propose a new network model in which all nodes are directly connected to the center node to keep the communication costs manageable. Based on the network model, we suggest a nodal distributed algorithm and direct communication to all nodes through the center node. We demonstrate that the suggested algorithm converges to a local minimum rather than a point, satisfying the first optimality condition.ResultsThe proposed algorithm identifies solutions to OPF problems in various IEEE model systems. The solutions are identical to those using a centrally optimized and heuristic approach. The computation time at each node does not depend on the system size, and Niter does not increase significantly with the system size.ConclusionOur proposed network model is a star network for maintaining the shortest node-to-node distances to allow a linear information exchange. The proposed algorithm guarantees the convergence to a local minimum rather than a maximum or a saddle point, and it maintains computational efficiency for a large-scale OPF, scalable algorithm.

Dataset Information

Optimal Experience and Optimal Identity: A Multinational Study of the Associations Between Flow and Social Identity.

Publications

Optimal Experience and Optimal Identity: A Multinational Study of the Associations Between Flow and Social Identity.

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