Complex Problem Solving after Unstructured Discussion: Effects of Information Distribution and Experience

Meyer, Bertolt; Schöll, Wolfgang

doi:10.1177/1368430209105045

Cited by 27 publications

(22 citation statements)

References 37 publications

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“…Funke, 1992;Funke & Frensch, 2007) • implicit knowledge (e.g. Berry, 1991;Berry & Broadbent, 1995;Buchner et al, 1995) defined as performance advantages in the accomplishment of cognitive requirements, which are based on an unconscious use of previously perceived and unintentionally stored information (Meyer & Scholl, 2009) • non-cognitive variables of personality and social factors such as self-confidence, perseverance, motivation, enjoyment (Dörner & Wearing, 1995).…”

Section: The Link Between Problem Solving In Technology-rich Environmmentioning

confidence: 99%

Socio-demographic and practice-oriented factors related to proficiency in problem solving: a lifelong learning perspective

Desjardins¹,

Эдерер

2015

International Journal of Lifelong Education

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This article explores the relative importance of different socio-demographic and practice-oriented factors that are related to proficiency in Problem Solving in Technology-Rich Environments (PSTRE) and by extension may be related to Complex Problem Solving (CPS). The empirical analysis focuses on the proficiency measurements of PSTRE made available by the Programme for the International Assessment of Adult Competencies (PIAAC), which may be relevant for gaining insight on some of the factors related to CPS. The purpose is to examine the relationship between the broad information processing experience that individuals gather in different contexts over the lifespan, and the chances to develop problem solving skills that adults receive in different sociodemographic profiles. Results reveal that socio-demographic factors such as age, education and immigration status as well as practice-oriented factors such as ICT use and reading practice at and outside work are strongly related to proficiency.

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Section: The Link Between Problem Solving In Technology-rich Environmmentioning

confidence: 99%

Socio-demographic and practice-oriented factors related to proficiency in problem solving: a lifelong learning perspective

Desjardins¹,

Эдерер

2015

International Journal of Lifelong Education

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“…Therefore, with reference to Meyer and Scholl (2009), we used the self-assessment of individuals' prior business economic knowledge as a coarse indicator. We asked participants to answer a single item in German language (''Please indicate your level of economic knowledge'') on a 5-point Likert scale.…”

Section: Self-assessment Economic Knowledge Item (Saeki)mentioning

confidence: 99%

“…The exploration phase of the Tailorshop was modified to include a reminder for CG2 and EG to make their periodic notes. Similar to previous studies (Danner et al 2011;Meyer and Scholl 2009), two dependent Tailorshop measures were analyzed: First, the variable difference in company value indicates participants' total gain or loss in company value between the beginning of the Tailorshop performance phase and its completion after 12 months. Second, the variable gain months indicates the number of months in which a participant managed to increase the company value during the performance phase.…”

Section: Tailorshopmentioning

confidence: 99%

Does visualization enhance complex problem solving? The effect of causal mapping on performance in the computer-based microworld Tailorshop

Öllinger

Hammon

Grundherr

et al. 2015

Education Tech Research Dev

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Causal mapping is often recognized as a technique to support strategic decisions and actions in complex problem situations. Such drawing of causal structures is supposed to particularly foster the understanding of the interaction of the various system elements and to further encourage holistic thinking. It builds on the idea that humans make use of mental maps to represent their environment and to make predictions about it. However, a profound theoretical underpinning and empirical research of the effects of causal mapping on problem solving is missing. This study compares a causal mapping approach with more common problem solving techniques utilizing the standardized computer-simulated microworld Tailorshop. Results show that causal mapping leads to a worse performance in managing the Tailorshop and was not associated with increased knowledge about the underlying system's structure. We conclude that the successful representation of the causal structure and the control of a complex scenario require the concerted interplay of cognitive skills that go beyond drawing causal maps.

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“…[2][3][4][5][6][7], the dimensions of the problem are slightly smaller, but are within the same order of magnitude (e.g. 15 (9) vs. 11 (7) control variables (integer) and 16 vs. 12 state variables per month).…”

Section: The Iwr Tailorshop-modelmentioning

confidence: 99%

“…The Tailorshop is sometimes referred to as the "Drosophila" for CPS researchers [1] and thus a prominent example for a computer-based microworld. It has been used in a large number of studies, e.g., [2][3][4][5][6][7]. Comprehensive reviews on studies with Tailorshop have been published, e.g., [1,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A decomposition approach for a new test-scenario in complex problem solving

Engelhart

Funke

Säger

2013

Journal of Computational Science

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Over the last years, psychological research has increasingly used computer-supported tests, especially in the analysis of complex human decision making and problem solving. The approach is to use computerbased test scenarios and to evaluate the performance of participants and correlate it to certain attributes, such as the participant's capacity to regulate emotions. However, two important questions can only be answered with the help of modern optimization methodology. The first one considers an analysis of the exact situations and decisions that led to a bad or good overall performance of test persons. The second important question concerns performance, as the choices made by humans can only be compared to one another, but not to the optimal solution, as it is unknown in general. Additionally, these test-scenarios have usually been defined on a trial-and-error basis, until certain characteristics became apparent. The more complex models become, the more likely it is that unforeseen and unwanted characteristics emerge in studies. To overcome this important problem, we propose to use mathematical optimization methodology not only as an analysis and training tool, but also in the design stage of the complex problem scenario.We present a novel test scenario, the IWR Tailorshop, with functional relations and model parameters that have been formulated based on optimization results. We also present a tailored decomposition approach to solve the resulting mixed-integer nonlinear programs with nonconvex relaxations and show some promising results of this approach.

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Complex Problem Solving after Unstructured Discussion: Effects of Information Distribution and Experience

Cited by 27 publications

References 37 publications

Socio-demographic and practice-oriented factors related to proficiency in problem solving: a lifelong learning perspective

Socio-demographic and practice-oriented factors related to proficiency in problem solving: a lifelong learning perspective

Does visualization enhance complex problem solving? The effect of causal mapping on performance in the computer-based microworld Tailorshop

A decomposition approach for a new test-scenario in complex problem solving

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