Complex Problem-solving Strategies Research Articles

Analysing Complex Problem-Solving Strategies from a Cognitive Perspective: The Role of Thinking Skills.

Complex problem solving (CPS) is considered to be one of the most important skills for successful learning. In an effort to explore the nature of CPS, this study aims to investigate the role of inductive reasoning (IR) and combinatorial reasoning (CR) in the problem-solving process of students using statistically distinguishable exploration strategies in the CPS environment. The sample was drawn from a group of university students (N = 1343). The tests were delivered via the eDia online assessment platform. Latent class analyses were employed to seek students whose problem-solving strategies showed similar patterns. Four qualitatively different class profiles were identified: (1) 84.3% of the students were proficient strategy users, (2) 6.2% were rapid learners, (3) 3.1% were non-persistent explorers, and (4) 6.5% were non-performing explorers. Better exploration strategy users showed greater development in thinking skills, and the roles of IR and CR in the CPS process were varied for each type of strategy user. To sum up, the analysis identified students’ problem-solving behaviours in respect of exploration strategy in the CPS environment and detected a number of remarkable differences in terms of the use of thinking skills between students with different exploration strategies.

Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction

Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model.

Tolerance for Uncertainty and Patterns of Decision-Making in Complex Problem-Solving Strategies

Current studies of complex problem-solving do not commonly evaluate the regulatory role of such personality-based variables as tolerance for uncertainty, risk-readiness, and patterns for coping with decisional conflict. This research aims to establish the contribution of those traits into individual parameters of complex problem-solving strategies. The study was conducted on 53 healthy individuals 17 to 29 years old (M = 20.42; SD = 2.34). Our own computerized complex problem task “The Anthill” was developed for this research. We identified five measurable parameters of the participants’ problem-solving strategies: preferred orientational level (POL); orientational level variability (OLV); class quotas‘ range (R); mean and median quotas shift (MS and MeS); and abrupt changes of strategy (AC). Psychodiagnostic methods included: new questionnaire of tolerance/intolerance for uncertainty; personal decision-making factors questionnaire; Melbourne Decision Making Questionnaire; Subjective Risk Intelligence Scale; Eysencks’ Impulsiveness Scale. The study showed the role of tolerance for uncertainty, risk-readiness, negative attitude toward uncertainty, and decision-making styles in the regulation of complex problem-solving strategies. Specifically, procrastination, tolerance for uncertainty, and risk-readiness were significant predictors of individual strategy indicators, such as POL, OLV, and MeS. Thus, personality traits were shown to regulate resource allocation strategies and the required level of orientation in a complex problem.

Transdisciplinary Design: Tamed complexity through new collaboration

The following essay aims to expose the characteristics of Transdisciplinary Design and its relationship with design thinking, applying both as complex problem-solving strategies. In order to achieve this, we must briefly explore the characteristics of design thinking and non-uni-disciplinary strategies and subsequently describe the reasons why design can be considered as a privileged platform for problem-solving strategies for the more complex problematics of our contemporary society; ranging from transdisciplinary design as well as the differences with regard to design thinking model. Keywords: transdisciplinarity, design, complexity, design thinking, non-disciplinary approaches.

Cracking her codes: understanding shared technology resources as positioning artifacts for power and status in CSCL environments

There is a positive relationship between student participation in computer-supported collaborative learning (CSCL) environments and improved complex problem-solving strategies, increased learning gains, higher engagement in the thinking of their peers, and an enthusiastic disposition toward groupwork. However, student participation varies from group to group, even in contexts where students and teachers have had extensive training in working together. In this study, we use positioning theory and interaction analysis to conceptualize and investigate relationships between student interactions across two partner pairs working with technology in an all-female cryptography summer camp and their negotiated positions of power and status. The analysis resulted in uneven participation patterns, unequal status orderings, and an imbalance of power in both comparison cases. We found a reflexive relationship between partner interactions around shared technology resources and negotiated positions of power and status, which leads us to conclude that interactions around technology function as an important indicator of negotiated positionings of power and status in CSCL settings, and vice-versa. With that said, we found qualitative differences in the ways emergent status problems impacted each team’s productivity with the cryptography challenge, which has important implications for future research on CSCL settings and classroom practice.

Relations Between Executive Functions and Different Symptomatic Dimensions in Obsessive Compulsive Disorder

There is no consensus in the literature as to neuropsychological functioning, the severity of obsessive-compulsive symptoms (OCS) and the definitions of the OCS dimensions. We conducted a cross-sectional study investigating the relationship between executive function and OCS severity in the various dimensions, according to the Dimensional Yale-Brown Obsessive-Compulsive Scale criteria. We evaluated 28 patients with OCS, using eight neuropsychological instruments to evaluate executive function. We found that OCS severity in the contamination/cleaning dimension correlates negatively with executive function, inhibitory control and attentional control. Severity in the hoarding dimension correlated positively with cognitive flexibility, visual processing and logical reasoning, whereas it correlated negatively with the capacity to develop efficient complex problem-solving strategies. There was also a positive correlation between severity in the symmetry/ordering dimension and attentional control. Our findings suggest that the profile of executive function in OCD is defined by the severity of the various OCS dimensions.

Fire and Ice

Extending research on complex problem solving (CPS) and dynamic decision making (DDM), this study investigated the relationship between horizontal-vertical individualism-collectivism, complex problem-solving strategies, and performance in two computer-simulated problem scenarios with different task demands. Participants were 535 students from Germany and the United States (both high on horizontal individualism), Brazil (high on horizontal collectivism), and India and the Philippines (both high on vertical collectivism). Path analyses revealed that horizontal individualism, horizontal collectivism, and vertical collectivism were related to action orientation, and action orientation and planning strategies were related to performance. Strategies were even stronger predictors of performance than the control variables computer experience and intelligence. The structural path model was confirmed for the overall sample and for each of the five national samples. Cross-cultural differences in the process model are discussed. Results showed the importance of cultural values in cognitive processing, specifically for the selection of CPS strategies.

Cross‐National Comparisons of Complex Problem‐Solving Strategies in Two Microworlds

Research in the fields of complex problem solving (CPS) and dynamic decision making using microworlds has been mainly conducted in Western industrialized countries. This study analyzes the CPS process by investigating thinking-aloud protocols in five countries. Participants were 511 students from Brazil, Germany, India, the Philippines, and the United States who worked on two microworlds. On the basis of cultural-psychological theories, specific cross-national differences in CPS strategies were hypothesized. Following theories of situatedness of cognition, hypotheses about the specific frequency of problem-solving strategies in the two microworlds were developed. Results of the verbal protocols showed (a) modification of the theoretical CPS model, (b) task dependence of CPS strategies, and (c) cross-national differences in CPS strategies. Participants' CPS processes were particularly influenced by country-specific problem-solving strategies.

The Search for Complex Problem-Solving Strategies in the Presence of Stressors

The present research tests the effects of time pressure and noise on open-mindedness to discover new problem-solving strategies. We are primarily interested in transfer of skill from one phase to the next. More specifically, this study investigates whether the presence of stressors makes participants adhere to the sustained use of complex rules. Participants learned to apply a complex rule in the first phase of a category learning task. In the second phase, this rule became dysfunctional and participants had to search for a new categorization rule in order to assign the stimuli to the correct classes. Two experiments were set up to investigate this issue. Participants were found to have difficulty discovering a complex Phase 2 rule in the presence of stressors, whereas the discovery of a simple rule was not hindered by the presence of stressors. In the discussion, it is argued that the present results are compatible with previous research on stressors showing that time pressure and noise induce the application of simple strategies. The innovative finding here is that this simplification also occurs in individuals who are accustomed to using complex solutions. The implications of the present results for emergency response training are elaborated upon.

The complex problem-solving competence of team coaches

Objectives The complexity and dynamics of team sports places high information-processing demands on coaches. They can meet these demands by applying either domain-specific or domain-unspecific problem-solving strategies. Method The first phase of this study used a domain-unspecific computer-simulated scenario (“heating oil company” [Hasselmann, D., & Strauss, B. (1993). Herausforderung Komplexität: Baustein 1 ( HEIZÖLHANDEL) [ The challenge of complexity: Building block 1 ( heating oil company)]. Hamburg, Germany: Windmühle]) to measure domain-unspecific complex problem-solving strategies in 38 top-league coaches (first or second German national league in team handball or basketball) and 43 local-league coaches. Results Results showed that top-league coaches exhibited better problem-solving performance than lower-league coaches in an unspecific domain. The second phase recorded the real-life coaching behavior of some of these coaches 6 months later during competitions and analyzed the content of their utterances. The main findings were that top-league coaches ( n=14) made fewer utterances in competitions, but gave relatively more concrete instructions compared with lower-league coaches ( n=13). Top-league coaches also criticized and motivated their players more frequently during play. Comparing intervention behavior across the two settings (computer-simulated scenario vs. competition) disclosed some general problem-solving strategies (e.g., number of motivating utterances, strength of interventions). Conclusion It is concluded that the achievement of sports coaches depends at least in part on their domain-general, complex problem-solving competence.

The economic effects of basic research: evidence for embodied knowledge transfer via scientists’ migration

The paper argues that a substantial proportion of the wider economic benefits to society from publicly-funded basic research is associated with scientists’ migration into the commercial sector of the innovation system. Rejecting a reduction of the research process to the propositional knowledge it produces, a set of hypotheses on the value of different types of knowledge is derived. The hypotheses are tested with empirical data obtained from scientists formerly employed by the Max Planck Society (MPS), one of the main organisations for basic research in the German innovation system. Findings indicate that rather than applying latest theoretical insights, scientists mainly transfer elements of knowledge that underlie complex problem-solving strategies in basic research.

Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results

We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, which was selected to represent properties that affect algorithm performance. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a ranking. The performance is assessed using a multicriteria evaluation measure that takes not only accuracy, but also time into account. As it is not common in Machine Learning to work with rankings, we had to identify and adapt existing statistical techniques to devise an appropriate evaluation methodology. Using that methodology, we show that the meta-learning method presented leads to significantly better rankings than the baseline ranking method. The evaluation methodology is general and can be adapted to other ranking problems. Although here we have concentrated on ranking classification algorithms, the meta-learning framework presented can provide assistance in the selection of combinations of methods or more complex problem solving strategies.