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Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development (OECD, 2014). doi: 10.1016/j.lindif.20 Cross Ref Full Text | Google Scholar Molnàr, G., Greiff, S., Wüstenberg, S., and Fischer, A. “Empirical study of computer-based assessment of domain-general complex problem-solving skills,” in The Nature of Problem Solving: Using research to Inspire 21st Century Learning, eds B. According to the World Economic Forum, CPS is one of the most important competencies required in the future (World Economic Forum, 2015).
This type of problem is called a “complex problem” and is of central importance to this paper.
All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).
This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct.
We start this article with a historical review before attempting to systematize different positions. The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986). Linking complex problem solving and general mental ability to career advancement: does a transversal skill reveal incremental predictive validity?
Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds.
For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993, for a discussion of this methodology). Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III III). According to the instructions, moving one of the matchsticks will make the equations true. doi: 10.1037/a0017815 Pub Med Abstract | Cross Ref Full Text | Google Scholar Ramnarayan, S., Strohschneider, S., and Schaub, H. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III III) are defined clearly. Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. doi: 10.1037/0033-295X.84.3.231 Pub Med Abstract | Cross Ref Full Text | Google Scholar Osman, M. Controlling uncertainty: a review of human behavior in complex dynamic environments. Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers (Blech and Funke, 2010; Funke, 2010; Knauff and Wolf, 2010; Leutner et al., 2012; Selten et al., 2012; Wüstenberg et al., 2012; Greiff et al., 2013b; Fischer and Neubert, 2015; Schoppek and Fischer, 2015), papers about measurement issues (Danner et al., 2011a; Greiff et al., 2012, 2015a; Alison et al., 2013; Gobert et al., 2015; Greiff and Fischer, 2013; Herde et al., 2016; Stadler et al., 2016), papers about applications (Fischer and Neubert, 2015; Ederer et al., 2016; Tremblay et al., 2017), papers about differential effects (Barth and Funke, 2010; Danner et al., 2011b; Beckmann and Goode, 2014; Greiff and Neubert, 2014; Scherer et al., 2015; Meißner et al., 2016; Wüstenberg et al., 2016), one paper about developmental effects (Frischkorn et al., 2014), one paper with a neuroscience background (Osman, 2012), papers about cultural differences (Güss and Dörner, 2011; Sonnleitner et al., 2014; Güss et al., 2015), papers about validity issues (Goode and Beckmann, 2010; Greiff et al., 2013c; Schweizer et al., 2013; Mainert et al., 2015; Funke et al., 2017; Greiff et al., 2017, 2015b; Kretzschmar et al., 2016; Kretzschmar, 2017), review papers and meta-analyses (Osman, 2010; Stadler et al., 2015), and finally books (Qudrat-Ullah, 2015; Csapó and Funke, 2017b) and book chapters (Funke, 2012; Hotaling et al., 2015; Funke and Greiff, 2017; Greiff and Funke, 2017; Csapó and Funke, 2017a; Fischer et al., 2017; Molnàr et al., 2017; Tobinski and Fritz, 2017; Viehrig et al., 2017). Predicting complex problem solving and school grades with working memory and ability self-concept. As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes (Schraw et al., 1995; but see Funke, 2010). Influence of goal setting on performance, intrinsic motivation, processing style, and affect in a complex problem solving task. Well-defined problems have a clear set of means for reaching a precisely described goal state.