A Computational Theory of Complex Problem Solving Using Latent Semantic Analysis

Exploring the Relation between Time on Task and Ability in Complex Problem Solving

Metacognitive confidence judgments and their link to complex problem solving

In recent decades, the types of problems encountered in daily life have changed considerably. In particular, the technology-based society of today increasingly requires people to deal with dynamically changing, intransparent, and complex problems that cannot be solved only by applying factual knowledge. Moreover, in education and at work, such complex problems often need to be addressed collaboratively. Problem solving skills belong to the skills needed to successfully master such problems and are therefore considered to be crucial in the twenty-first century, but we can differentiate between problem solving on an individual level and collaborative problem solving. In the first part of this chapter, we provide insight into individual complex problem solving (CPS) research, with a special focus on its assessment. Specifically, we elaborate on different computer-based approaches to the measurement of CPS and their benefits and limitations. On the basis of the results of various empirical studies, we describe to which extent the CPS assessment has an added value in explaining the scholastic achievement and offer insights into a particular development of the assessment of this skill in large-scale educational contexts. Specifically, we elaborate on the assessment of CPS in the most influential large-scale project worldwide – the Programme for International Student Assessment (PISA) – and illustrate the process by describing example tasks. Further, we elaborate on how research on CPS has expanded towards the conceptually related skill of collaborative problem solving (ColPS) and discuss specific challenges in its assessment. With respect to the recent inclusion of ColPS in large-scale assessments, we present the assessment approach of another large-scale worldwide project – The Assessment and Teaching of Twenty-first Century Skills (ATC21S) – and describe how ColPS was assessed in the PISA 2015 cycle. Finally, we outline the importance of problem solving concepts in general and CPS specifically for future research, and discuss open research questions in this field.

This thesis analyzes the influence of structural knowledge on the individual level and the influence of knowledge heterogeneity on the group level on complex problem solving (CPS) performance. For the elicitation of structural knowledge, a computer based method, the association structure test (AST), is developed. Through term associations, measurement of thinking times, and through pairwise concept comparisons, the AST elicits a graph for each participant. The AST is tested in the domain of CPS. As complex problems are usually addressed by groups, a group setting is chosen. A curvilinear n-shaped connection between the group''s knowledge heterogeneity and its CPS performance is assumed. In an experiment, 150 participants were divided into dyads. Each participant received a text with seven knowledge elements on the control of a complex scenario. The heterogeneity of knowledge in a dyad was varied (small, medium, and large). After learning, dyad members self-assessed their knowledge. Knowledge similarity was calculated with knowledge management system (the skillMap). The knowledge similarity was also used for performance prediction. A discussion followed, during which dyad members taught each other what they had learned. Their structural knowledge was then assessed with the AST. In the following CPS task, dyads with medium heterogeneity exhibited a significant superior performance in comparison with the other two conditions. Knowledge heterogeneity exhibited a curvilinear relationship with the dyad''s CPS performance. The weighted density of AST-elicited knowledge graphs weakly correlated with CPS performance and explained a small but unique fraction of its variance. The skillMap similarity measure correlated significantly with CPS performance. Computer-based knowledge elicitation tools are thus potentially suited for performance prediction. CPS performance of groups is partially determined by the way in which knowledge is distributed inside the group.

Research on complex problem solving (CPS) has reached a stage where certain standards have been achieved, whereas the future development is quite ambiguous. In this situation, the editors of the Journal of Dynamic Decision Making asked a number of representative authors to share their point of view with respect to seven questions about the relevance of (complex) problem solving as a research area, about the contribution of laboratory-based CPS research to solving real life problems, about the roles of knowledge, strategies, and intuition in CPS, and about the existence of expertise in CPS.

Research on complex problem solving (CPS) has reached a stage where certain standards have been achieved, whereas the future development is quite ambiguous. In this situation, the editors of the Journal of Dynamic Decision Making asked a number of representative authors to share their point of view with respect to seven questions about the relevance of (complex) problem solving as a research area, about the contribution of laboratory-based CPS research to solving real life problems, about the roles of knowledge, strategies, and intuition in CPS, and about the existence of expertise in CPS.

Complex problem analysis and solving are an important evolutionary direction of TRIZ. Identifying core root cause and confirming contradictions are important for the management of complex problem solving. Both Theory of Constraints (TOC) and Fault Tree Analysis (FTA) include tools which can help get root cause from complex problem situation. FTA provides qualitative and quantitative analysis methods to evaluate the weights of root causes. A complex problem solving model integrating tools of TOC and FTA in TRIZ frame is put forward, a case shows the application of this model.

Innovative assessments of cross-curricular competencies such as complex problem solving (CPS) have currently received considerable attention in large-scale educational studies. This study investigated the nature of CPS by applying a state-of-the-art approach to assess CPS in high school. We analyzed whether two processes derived from cognitive psychology, knowledge acquisition and knowledge application, could be measured equally well across grades and how these processes differed between grades. Further, relations between CPS, general mental ability (g), academic achievement, and parental education were explored. Hungarian high school students in Grades 5 to 11 (N 855) completed MicroDYN, which is a computer-based CPS test, and the Culture Fair Test 20-R as a measure of g. Results based on structural equation models showed that empirical modeling of CPS was in line with theories from cognitive psychology such that the two dimensions identified above were found in all grades, and that there was some development of CPS in school, although the Grade 9 students deviated from the general pattern of development. Finally, path analysis showed that CPS was a relevant predictor of academic achievement over and above g. Overall, results of the current study provide support for an understanding of CPS as a cross-curricular skill that is accessible through computer-based assessment and that yields substantial relations to school performance. Thus, the increasing attention CPS has currently received on an international level seems warranted given its high relevance for educational psychologists.

<span lang="EN-US">Complex problem solving (CPS) is a new paradigm in solving problems and is one of the soft skills needed to face the industrial revolution 4.0. Reflective abstraction is associating and modifying pre-existing conceptions into new situations. This article reviews research on CPS and reflective abstraction. This research is needed to know the relationship between reflective abstraction and CPS. The systematic writing of this review was assisted by the Publish or Perish 7 application, Mendeley, and VOSviewer. A literature search was performed through the ScienceDirect and ERIC databases. Based on the search results with the term “complex problem solving” and several exclusion criteria, 58 articles were found, whereas with the word “reflective abstraction” there were 23 articles, totaling 81 papers. Based on the literature review, it was found that there is a relationship between CPS and reflective abstraction by obtaining common ground in the form of prior knowledge. CPS requires prior knowledge from reflective abstraction to integrate the most relevant information. To improve CPS, efforts and special attention can be made to build initial knowledge through reflective abstraction. This article contributes to further research and becomes a study for the themes of CPS and reflective abstraction in learning and education.</span>

Influence of curriculum reform: An analysis of student mathematics achievement in Mainland China

Complex problem solving — More than reasoning?

Granular computing is to imitate human’s multi-granular computing strategy to problem solving in order to endow computers with the same capability. Its final goal is to reduce the computational complexity. To the end, based on the simplicity principle the problem at hand should be represented as simpler as possible. From structural information theory, it’s known that if a problem is represented at different granularities, the hierarchical description of the problem will be a simpler one. The simpler the representation the lower the computational complexity of problem solving should be. We presented a quotient space theory to multi-granular computing. Based on the theory a problem represented by quotient spaces will have a hierarchical structure. Therefore, the quotient space based multi-granular computing can reduce the computational complexity in problem solving. In the talk, we’ll discuss how the hierarchical representation can reduce the computational complexity in problem solving by using some examples.

Complexity manifests itself in many ways when attempting to solve different problems, and different tools are needed to deal with the different dimensions underlying that complexity. Not all complexity is created equal. We find that most treatments of complexity in problem solving within both the statistical and quality literature focus narrowly on technical complexity, which includes complexity of subject matter knowledge as well as complexity in the data access and analysis of that data. The literature lacks an understanding of how political complexity or organizational complexity interferes with good technical solutions when trying to deploy a solution. Therefore, people trained in statistical problem solving are ill-prepared for the situations they are likely to face on real projects. We propose a framework that illustrates examples of complexity from our own experiences, and the literature. This framework highlights the need for more holistic problem-solving approaches and a broader view of complexity. We also propose approaches to successfully navigate complexity.

Verification of algebra step problems: A chronometric study of human problem solving

Today, much problem solving is performed by teams, rather than individuals. The complexity of these problems has exceeded the cognitive capacity of any individual and requires a team of members to solve them. The success of solving these complex problems not only relies on individual team members who possess different but complementary expertise, but more importantly, their collective problem solving ability. To better conceptualize large scale complex problem solving, an understanding of collective cognitive components and processes during team-based complex problem solving is necessary. This paper offers a conceptual discussion about complex problem solving from a collective cognition perspective. The types of cognitive processing and cognitive components of team-based problem solving (TBPS) as well as the cognitive states of collective emergent cognitive states and the interactive mechanisms will be discussed. Also, implications from the model for assessing TBPS performance and suggestions for future research will be offered.