Intelligence in action – Effective strategic behaviors while solving complex problems

Although individual differences in complex problem solving (CPS) are well–established, relatively little is known about the process demands that are common to different dynamic control (CDC) tasks. A prominent example is the VOTAT strategy that describes the separate variation of input variables (“Vary One Thing At a Time”) for analyzing the causal structure of a system. To investigate such comprehensive knowledge elements and strategies, we devised the real-time driven CDC environment Dynamis2 and compared it with the widely used CPS test MicroDYN in a transfer experiment. One hundred sixty five subjects participated in the experiment, which completely combined the role of MicroDYN and Dynamis2 as source or target problem. Figural reasoning was assessed using a variant of the Raven Test. We found the expected substantial correlations among figural reasoning and performance in both CDC tasks. Moreover, MicroDYN and Dynamis2 share 15.4% unique variance controlling for figural reasoning. We found positive transfer from MicroDYN to Dynamis2, but no transfer in the opposite direction. Contrary to our expectation, transfer was not mediated by VOTAT but by an approach that is characterized by setting all input variables to zero after an intervention and waiting a certain time. This strategy (called PULSE strategy) enables the problem solver to observe the eigendynamics of the system. We conclude that for the study of complex problem solving it is important to employ a range of different CDC tasks in order to identify components of CPS. We propose that besides VOTAT and PULSE other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated. The positive transfer from MicroDYN to the more complex and dynamic Dynamis2 suggests an application of MicroDYN as training device.

Section: Introductionmentioning

confidence: 91%

Common Process Demands of Two Complex Dynamic Control Tasks: Transfer Is Mediated by Comprehensive Strategies

Schoppek

Fischer

2017

“…Nevertheless, the MCS tasks are very similar to each other and implement only a small number of CPS characteristics, giving the subtests high internal consistencies. Specifically, all minimal systems can be fully explored with the simple strategy “vary one thing at a time” (VOTAT; e.g., Vollmeyer et al, 1996) or the closely related strategy “vary one or none at a time” (Beckmann and Goode, 2014; for additional distinctions see Lotz et al, 2017). No special training is necessary to learn these strategies.…”

Section: Part Ii: Review and Critique Of The Minimally Complex Systemmentioning

confidence: 99%

Impact of Cognitive Abilities and Prior Knowledge on Complex Problem Solving Performance – Empirical Results and a Plea for Ecologically Valid Microworlds

Süß

Kretzschmar

2018

The original aim of complex problem solving (CPS) research was to bring the cognitive demands of complex real-life problems into the lab in order to investigate problem solving behavior and performance under controlled conditions. Up until now, the validity of psychometric intelligence constructs has been scrutinized with regard to its importance for CPS performance. At the same time, different CPS measurement approaches competing for the title of the best way to assess CPS have been developed. In the first part of the paper, we investigate the predictability of CPS performance on the basis of the Berlin Intelligence Structure Model and Cattell’s investment theory as well as an elaborated knowledge taxonomy. In the first study, 137 students managed a simulated shirt factory (Tailorshop; i.e., a complex real life-oriented system) twice, while in the second study, 152 students completed a forestry scenario (FSYS; i.e., a complex artificial world system). The results indicate that reasoning – specifically numerical reasoning (Studies 1 and 2) and figural reasoning (Study 2) – are the only relevant predictors among the intelligence constructs. We discuss the results with reference to the Brunswik symmetry principle. Path models suggest that reasoning and prior knowledge influence problem solving performance in the Tailorshop scenario mainly indirectly. In addition, different types of system-specific knowledge independently contribute to predicting CPS performance. The results of Study 2 indicate that working memory capacity, assessed as an additional predictor, has no incremental validity beyond reasoning. We conclude that (1) cognitive abilities and prior knowledge are substantial predictors of CPS performance, and (2) in contrast to former and recent interpretations, there is insufficient evidence to consider CPS a unique ability construct. In the second part of the paper, we discuss our results in light of recent CPS research, which predominantly utilizes the minimally complex systems (MCS) measurement approach. We suggest ecologically valid microworlds as an indispensable tool for future CPS research and applications.

“…A large number of studies have highlighted the importance and advantages of technology-based assessment over traditional paper-based testing (Csapó et al, 2012 ). Three main factors support and motivate the use of technology in educational assessment: (1) the improved efficiency and greater measurement precision in the already established assessment domains (e.g., Csapó et al, 2014 ); (2) the possibility of measuring constructs that would be impossible to measure by other means (e.g., Complex Problem Solving (CPS) 1 ; see Greiff et al, 2012 , 2013 ); and (3) the opportunity of logging and analyzing not only observed variables, but metadata as well (Lotz et al, 2017 ; Tóth et al, 2017 ; Zoanetti and Griffin, 2017 ). Analyzing logfiles may contribute to a deeper and better understanding of the phenomenon under examination.…”

Section: Introductionmentioning

confidence: 99%

The Efficacy and Development of Students' Problem-Solving Strategies During Compulsory Schooling: Logfile Analyses

Molnár

Csapó

2018

The purpose of this study was to examine the role of exploration strategies students used in the first phase of problem solving. The sample for the study was drawn from 3rd- to 12th-grade students (aged 9–18) in Hungarian schools (n = 4,371). Problems designed in the MicroDYN approach with different levels of complexity were administered to the students via the eDia online platform. Logfile analyses were performed to ascertain the impact of strategy use on the efficacy of problem solving. Students' exploration behavior was coded and clustered through Latent Class Analyses. Several theoretically effective strategies were identified, including the vary-one-thing-at-a-time (VOTAT) strategy and its sub-strategies. The results of the analyses indicate that the use of a theoretically effective strategy, which extract all information required to solve the problem, did not always lead to high performance. Conscious VOTAT strategy users proved to be the best problem solvers followed by non-conscious VOTAT strategy users and non-VOTAT strategy users. In the primary school sub-sample, six qualitatively different strategy class profiles were distinguished. The results shed new light on and provide a new interpretation of previous analyses of the processes involved in complex problem solving. They also highlight the importance of explicit enhancement of problem-solving skills and problem-solving strategies as a tool for knowledge acquisition in new contexts during and beyond school lessons.