scholarly journals Complex Problems in Entrepreneurship Education: Examining Complex Problem-Solving in the Application of Opportunity Identification

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yvette Baggen ◽  
Jakob Mainert ◽  
André Kretzschmar ◽  
Thomas Lans ◽  
Harm J. A. Biemans ◽  
...  
Keyword(s):  
Problem Solving ◽  
Entrepreneurship Education ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Conceptual Level ◽  
Opportunity Identification ◽  
Domain Specific ◽  
Masters Students ◽  
Opening Up ◽  
General Skill

In opening up the black box of what entrepreneurship education (EE) should be about, this study focuses on the exploration of relationships between two constructs: opportunity identification (OI) and complex problem-solving (CPS). OI, as a domain-specific capability, is at the core of entrepreneurship research, whereas CPS is a more domain-general skill. On a conceptual level, there are reasons to believe that CPS skills can help individuals to identify potential opportunities in dynamic and nontransparent environments. Therefore, we empirically investigated whether CPS relates to OI among 113 masters students. Data is analyzed using multiple regressions. The results show that CPS predicts the number of concrete ideas that students generate, suggesting that having CPS skills supports the generation of detailed, potential business ideas of good quality. The results of the current study suggest that training CPS, as a more domain-general skill, could be a valuable part of what should be taught in EE.

scholarly journals Linking complex problem solving to opportunity identification competence within the context of entrepreneurship

2015 ◽  
Vol 34 (4) ◽  
pp. 412-429 ◽  
Author(s):  
Yvette Baggen ◽  
Jakob Mainert ◽  
Thomas Lans ◽  
Harm J. A. Biemans ◽  
Samuel Greiff ◽  
...  
Keyword(s):  
Problem Solving ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Opportunity Identification

Extending the Assessment of Complex Problem Solving to Finite State Automata

2015 ◽  
Vol 31 (3) ◽  
pp. 181-194 ◽  
Author(s):  
Jonas C. Neubert ◽  
André Kretzschmar ◽  
Sascha Wüstenberg ◽  
Samuel Greiff
Keyword(s):  
Problem Solving ◽  
Complex Problem ◽  
Assessment Instruments ◽  
Finite State Automata ◽  
Complex Problem Solving ◽  
Domain Specific ◽  
Complex Problems ◽  
Specific Subset ◽  
Formal Framework ◽  
Finite State

Abstract. Recent advancements in the assessment of Complex Problem Solving (CPS) build on the use of homogeneous tasks that enable the reliable estimation of CPS skills. The range of problems featured in established instruments such as MicroDYN is consequently limited to a specific subset of homogeneous complex problems. This restriction is problematic when looking at domain-specific examples of complex problems, which feature characteristics absent from current assessment instruments (e.g., threshold states). We propose to utilize the formal framework of Finite State Automata (FSA) to extend the range of problems included in CPS assessment. An approach based on FSA, called MicroFIN, is presented, translated into specific tasks, and empirically investigated. We conducted an empirical study (N = 576), (1) inspecting the psychometric features of MicroFIN, (2) relating it to MicroDYN, and (3) investigating the relations to a measure of reasoning (i.e., CogAT). MicroFIN (1) exhibited adequate measurement characteristics and multitrait-multimethod models indicated (2) the convergence of latent dimensions measured with MicroDYN. Relations to reasoning (3) were moderate and comparable to the ones previously found for MicroDYN. Empirical results and corresponding explanations are discussed. More importantly, MicroFIN highlights the feasibility of expanding CPS assessment to a larger spectrum of complex problems.

scholarly journals Developing Complex Problem-Solving Skills: An Engineering Perspective

2019 ◽  
Vol 12 (3) ◽  
pp. 82
Author(s):  
Sigrid Schefer-Wenzl ◽  
Igor Miladinovic
Keyword(s):  
Problem Solving ◽  
Software Design ◽  
Course Design ◽  
Complex Problem ◽  
Work Environments ◽  
Complex Problem Solving ◽  
Specific Context ◽  
Problem Solving Skills ◽  
Domain Specific ◽  
The Core

While technical skills remain the core foundation for engineering graduates, professional competencies, including communication, teamwork, and complex problem solving, are increasingly important to succeed in work environments. To enhance the employability of our students, we have integrated several professional skills courses into our “Software Design and Engineering” Master study curriculum, and combined each of them with a technical course. In this paper, we present a blended-learning course design including didactic methods for teaching complex problem solving. The course is intertwined with a lecture on software integration topics, which enables the students to apply their complex problem solving skills on real projects in a domain-specific context.

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.

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