On Some Lessons from Modeling Contexts in Complex Problem Solving in Information Technology

2013 ◽  
Vol 6 (4) ◽  
pp. 55-70 ◽  
Author(s):  
D. Petkov ◽  
O. Petkova ◽  
T. Andrew
Keyword(s):  
Information Technology ◽  
Problem Solving ◽  
Software Engineering ◽  
Systems Thinking ◽  
Complex Problem ◽  
Problem Structuring Methods ◽  
Two Dimensional ◽  
Complex Problem Solving ◽  
Problem Structuring ◽  
Management Interventions

This paper reviews current research on context in problem solving and existing two-dimensional frameworks for expressing project contexts in Systems Thinking and Software Engineering. It makes the case for modeling of context with Problem Structuring Methods. The authors present lessons learnt from applying such methods in the context of their experience with several complex management interventions in Information and Telecommunications Technologies. The paper aims to contribute to the understanding of project contexts in complex problem solving in Information Technology.

scholarly journals Development of Perceived Complex Problem-Solving Instrument in Domain of Complex Systems

Systems ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 51
Author(s):  
Morteza Nagahi ◽  
Alieh Maddah ◽  
Raed Jaradat ◽  
Mohammad Mohammadi
Keyword(s):  
Problem Solving ◽  
Complex Systems ◽  
Systems Thinking ◽  
21St Century ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Final Model ◽  
Complex Problems ◽  
High Level ◽  
The Relationship

The ability to solve modern complex systems becomes a necessity of the 21st century. The purpose of this study is the development of an instrument that measures an individual’s perception toward solving complex problems. Based on literature and definitions, an instrument with four stages named perceived complex problem-solving (PCPS) was designed through exploratory and confirmatory stages. The instrument is validated and scaled through different models, and the final model is discussed. After completing validation and scale development of the PCPS instrument, the final model of the PCPS instrument was introduced to resolve the gap in the literature. The final model of the PCPS instrument is able to find and quantify the degree of perception an individual holds in dealing with complex problems and can be utilized in different settings and environments. Further research about the relationship between Systems Thinking and CPS revealed individuals with a high level of systems thinking have a better understanding of the characteristics of complex problems and so better perception of CPS.

Complex Problem Solving and Worked Examples

2009 ◽  
Vol 23 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Florian Schmidt-Weigand ◽  
Martin Hänze ◽  
Rita Wodzinski
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Experience ◽  
Worked Examples ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Strategic Learning ◽  
Learning Behavior ◽  
Worked Example ◽  
8Th Grade

How can worked examples be enhanced to promote complex problem solving? N = 92 students of the 8th grade attended in pairs to a physics problem. Problem solving was supported by (a) a worked example given as a whole, (b) a worked example presented incrementally (i.e. only one solution step at a time), or (c) a worked example presented incrementally and accompanied by strategic prompts. In groups (b) and (c) students self-regulated when to attend to the next solution step. In group (c) each solution step was preceded by a prompt that suggested strategic learning behavior (e.g. note taking, sketching, communicating with the learning partner, etc.). Prompts and solution steps were given on separate sheets. The study revealed that incremental presentation lead to a better learning experience (higher feeling of competence, lower cognitive load) compared to a conventional presentation of the worked example. However, only if additional strategic learning behavior was prompted, students remembered the solution more correctly and reproduced more solution steps.

The Prediction of Problem-Solving Assessed Via Microworlds

2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Complex Problem ◽  
Two Dimensions ◽  
Assessment Instruments ◽  
Complex Problem Solving ◽  
Visual Spatial ◽  
Rule Knowledge ◽  
Rule Application ◽  
Fluid Reasoning

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.

scholarly journals Working Memory, Fluid Reasoning, and Complex Problem Solving: Different Results Explained by the Brunswik Symmetry

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
André Kretzschmar ◽  
Stephan Nebe
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Complex Problem ◽  
Equation Modeling ◽  
Complex Problem Solving ◽  
Symmetry Principle ◽  
Fluid Reasoning ◽  
The Impact

In order to investigate the nature of complex problem solving (CPS) within the nomological network of cognitive abilities, few studies have simultantiously considered working memory and intelligence, and results are inconsistent. The Brunswik symmetry principle was recently discussed as a possible explanation for the inconsistent findings because the operationalizations differed greatly between the studies. Following this assumption, 16 different combinations of operationalizations of working memory and fluid reasoning were examined in the present study (N = 152). Based on structural equation modeling with single-indicator latent variables (i.e., corrected for measurement error), it was found that working memory incrementally explained CPS variance above and beyond fluid reasoning in only 2 of 16 conditions. However, according to the Brunswik symmetry principle, both conditions can be interpreted as an asymmetrical (unfair) comparison, in which working memory was artificially favored over fluid reasoning. We conclude that there is little evidence that working memory plays a unique role in solving complex problems independent of fluid reasoning. Furthermore, the impact of the Brunswik symmetry principle was clearly demonstrated as the explained variance in CPS varied between 4 and 31%, depending on which operationalizations of working memory and fluid reasoning were considered. We argue that future studies investigating the interplay of cognitive abilities will benefit if the Brunswik principle is taken into account.

The Role of Strategy Knowledge for the Application of Strategies in Complex Problem Solving Tasks

2014 ◽  
Vol 19 (1-2) ◽  
pp. 127-146 ◽  
Author(s):  
Sascha Wüstenberg ◽  
Matthias Stadler ◽  
Jarkko Hautamäki ◽  
Samuel Greiff
Keyword(s):  
Problem Solving ◽  
Complex Problem ◽  
Complex Problem Solving
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