26 papers · ranked by Valyu relevance
Dietrich Dörner, Joachim Funke
Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems.…
Qianli Yang, Zhihua Zhu, Ruoguang Si, Yunwei Li + 2 more
Human intelligence is characterized by our remarkable ability to solve complex problems. This involves planning a sequence of actions that leads us from an initial state to a desired goal state. Quantifying and comparing problem-solving capabilities across species and finding its evolutional roots is a fundamental…
Vasanth Sarathy
Real world problem-solving (RWPS) is what we do every day. It requires flexibility, resilience, resourcefulness, and a certain degree of creativity. A crucial feature of RWPS is that it involves continuous interaction with the environment during the problem-solving process. In this process, the environment can be seen…
Patrick Kyllonen, Cristina Anguiano Carrasco, Harrison J. Kell
Complex problem solving (CPS) has emerged over the past several decades as an important construct in education and in the workforce. We examine the relationship between CPS and general fluid ability (Gf) both conceptually and empirically. A review of definitions of the two factors, prototypical tasks, and the…
Juliana Gottschling, Florian Krieger, Samuel Greiff
The development of a vaccine marks a breakthrough in the fight against infectious diseases. However, to eradicate highly infectious diseases globally, the immunization of large parts of the population is needed. Otherwise, diseases, such as polio, measles, or more recently COVID-19, will repeatedly flare-up, with…
Michael Gr. Voskoglou, Sheryl Buckley
Computational thinking is a new problem solving method named for its extensive use of computer science techniques. It synthesizes critical thinking and existing knowledge and applies them to solve complex technological problems. The term was coined by J. Wing [1], but the relationship between computational and critical…
K. Kells
A practical problem-solving framework is proposed for multi-stakeholder initiative (MSI) problemsolving processes involving socio-ecological systems (SES)—so-called wicked problems—based on insights borrowed from a model of the individual human, cognitive problem-solving process. The disciplined facilitation of the…
Oka Kurniawan, Cyrille Jégourel, Norman Tiong Seng Lee, Matthieu De Mari + 1 more
'Matthieu De Mari' 'Christopher M. Poskitt'] Novice programmers often struggle with problem solving due to the high cognitive loads they face. Furthermore, many introductory programming courses do not explicitly teach it, assuming that problem solving skills are acquired along the way. In this paper, we present…
Chandralekha Singh, H. M. Oluseyi
One finding of cognitive research is that people do not automatically acquire usable knowledge by spending lots of time on task. Because students' knowledge hierarchy is more fragmented, "knowledge chunks" are smaller than those of experts. The limited capacity of short term memory makes the cognitive load high during…
František Ďuriš
The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms…
Mattia Eluchans, Gian Luca Lancia, Antonella Maselli, Marco D’Alessando + 2 more
We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants…
Kazuaki Kojima, Kazuhisa Miwa, Tatsunori Matsui
When using mathematics to solve problems in everyday life, problem solvers must recognize and formulate problems by themselves because structured problems are not provided. Therefore, in general education, fostering learner problem posing is an important task. Because novice learners have difficulty in composing…
Gülce Kardeş, David Krakauer, Joshua Grochow
Cognitive science and theoretical computer science both seek to classify and explain the difficulty of tasks. Mechanisms of intelligence can be understood as leading to reductions in task difficulty. We map concepts from the computational complexity of a physical puzzle, the Soma Cube, onto cognitive problem-solving…
Aminu Darda’u Rafindadi, Nasir Shafiq, Idris Othman, Miljan Mikić + 2 more
'Bochen Jia' 'Paul B. Tchounwou'] Cognitive failures at the information acquiring (safety training), comprehension, or application stages led to near-miss or accidents on-site. The previous studies rarely considered the cognitive processes of two different kinds of construction safety training. Cognitive processes are…
Reiji Ohkuma, Yuto Kurihara, Toru Takahashi, Rieko Osu
People solve insight problems that they encounter daily with a sudden sense of ‘aha!’ to reach a solution. Chunk decomposition, which decomposes the factors of the problem, and constraint relaxation, which manipulates filters to organize the information necessary to solve the problem, are important in insight…
Stephanie M. Halmo, Kira A. Yamini, Julie Dangremond Stanton
Stronger metacognitive regulation skills are linked to increased academic achievement. Metacognition has primarily been studied using retrospective methods, but these methods limit access to students’ in-the-moment metacognition. We investigated first-year life science students’ in-the-moment metacognition while they…
Authors not listed
In philosophy and science, a first principle is a basic proposition or assumption that cannot be deduced from any other proposition or assumption. Ancient Greek philosophy Aristotle defined the first principle as “the first basis from which a thing is known.” First principles thinking (or reasoning from first…
Juan P. Franco, Karlo Doroc, Nitin Yadav, Peter Bossaerts + 1 more
The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of…
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Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
Juan Pablo Franco, Peter Bossaerts, Carsten Murawski
Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which…
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The Socratic method, grounded in iterative questioning and critical dialogue, offers a compelling framework for leveraging large language models (LLMs) to advance scientific reasoning and discovery in chemistry and materials science. In this paper, we explore how Socratic principles can be integrated into prompt…
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Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
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Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Timothy Gould
This study examined factors influencing student confidence and their perception of learning in the context of undergraduate chemistry and biochemistry courses. Anonymous online surveys were used to measure the extent to which small group work influenced student confidence in solving problems compared to working…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Authors not listed
Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture…