Exploring Science: The Cognition and Development of Discovery Processes

By Howard Greisdorf. J. Documentation, 2003

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/999347beb4bd44f924c6dfe429e83fea022148a7

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Citations in the corpus

How Development and Personality Influence Scientific Thought, Interest, and Achievement (2006, background): “1 For sake of space, one developmental topic I leave for another publication is the evidence for and theory on how children use first principles in distinct domains of thought and can be viewed very much as implicit (folk) psychologists, physicists, biologists, and mathematicians (see Feist, 2006; F”
Phases of inquiry-based learning: Definitions and the inquiry cycle (2015, methodology): “justifying them (Klahr, 2000; National Research Council, 1996), Identifying a question, Accessing data of their choice to address the question, Analysing these data to identify”
The Oxford handbook of thinking and reasoning (2012, methodology): “, & Klahr, D. (1995). A 4-space model of scientific discovery.”
Contribution of Meta‐strategic Knowledge to Scientific Inquiry Learning (2009, unknown): “…in scientific thinking and accordantly in inquiry-based learning (Chen & Klahr, 1999; Chinn & Brewer, 1993; De Jong & Van Joolingen, 1998; Dunbar, 1993; Germann, Aram, Odom, & Burke, 1996; Klahr & Dunbar, 1988; Klahr, Fay, & Dunbar, 1993; Klahr, 2000; Koslowski, 1996; *Corresponding author.”
The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence (2010, methodology): “, heuristics) need to be applied, including use of analogies or simple trial and error (Klahr, 2000).”
Shared scientific thinking in everyday parent‐child activity (2001, methodology): “We chose the that support family collaboration (Borun & Dristas, 1997; Crowley & Callanan, 1998) and in part because it characterized features of openended tasks that are often used in psychological research on scientific thinking (Klahr 2000).”
Modelling Chemical Reasoning to Predict and Invent Reactions Marwin (2016, background): “,mn)⇒ { complementary if L = 4n+2 where n ∈ N0 analogous if L = 4n (1) Here we define molecules that (given specific conditions) can react with each other as complementary in their reactivity.”
Connecting Scientific Reasoning and Causal Inference (2004, methodology): “In the SR work, theories have also been shown to have a decisive effect, not only on patterns of inference but also on investigative strategies (Klaczynski, 2000; Klahr, 2000; Klahr, Fay, & Dunbar, 1993; Kuhn et al., 1988; Kuhn et al., 1995; Schauble, 1990, 1996).”
Scientific Thinking and Reasoning (2012, background): “Kulkarni and Simon (1988) used a more historical approach to uncover the problem-solving heuristics that Krebs used in his discovery of the urea cycle.”
From Everyday to Scientific Observation: How Children Learn to Observe the Biologist’s World (2009, unknown): “Although considered powerful in everyday cultural contexts, when considered within scientific contexts, children’s observational skills are portrayed as unsystematic, unfocused, and unsustained (e.g., Chen & Klahr, 1999; D. Kuhn, 1989; D. Kuhn, Amsel, & O’Loughlin, 1988; D. Kuhn, Garica-Mila, & Ande”
What Needs to Develop in the Development of Inquiry Skills? (2008, methodology): “A number of researchers, such as Klahr (2000), and indeed the NRC (1996) curriculum standards themselves, characterize a complete inquiry cycle as comprising identification of a question or questions(s), design of an investigation to address them, examination and analysis of empirical data, and…”
Reasoning About Multiple Variables: Control of Variables Is Not the Only Challenge (2007, unknown): “children show competence in scientific method, with some researchers emphasizing early competence (Gopnik, Sobel, Schulz, & Glymour, 2001; Ruffman, Perner, Olson, & Doherty, 1993; Schulz & Gopnik, 2004; Sodian, Zaitchik, & Carey, 1991), and others later lack of competence (Klaczynski, 2004; Klahr, 2”
Learning science through inquiry in kindergarten (2008, background): “…possess the cognitive resources of adults or scientists when it comes to designing controlled experiments and evaluating the fit of models to data (Klahr, 2000; Kuhn & Dean, 2004; Masnick & Klahr, 2003; Schauble, 1996) they can revise their concepts in the face of significant new evidence (Carey,…”
The impact of intellectual ability and metacognition on learning: New support for the threshold of problematicity theory (2006, background): “Second, evidence implies that the frontal lobes of the brain are of critical importance for human metacognition (Metcalfe, 1996; Shimamura, 1996, 2000), whereas cognitive operations are also located in other areas of the brain (Kalat, 1992; Posner, Petersen, Fox, & Raichle, 1988).”
A Scaffolding Design Framework for Software to Support Science Inquiry (2004, background): “For example, in our analysis, we found that one obstacle learners face involves difficulty in keeping track of plans and monitoring progress (Klahr, 2000; Schauble, Glaser, Raghavan, & Reiner, 1991).”
How Parent Explanation Changes What Children Learn from Everyday Scientific Thinking. (2007, methodology): “Developments in evidence collection and evaluation are often described as internal metacognitive advances enabling children to monitor the systematicity of evidence collection, to seek out the most informative kinds of evidence and comparisons, to draw valid inferences about evidence, and to constru”
The emergence of music from the Theory of Mind (2009, background): “The first route, called the ‘child as scientist’ explanation, is an ontogenetic selective process whereby developing individuals test theories empirically and discard those that are unsuccessful (Gopnik & Meltzoff, 1997; Klahr, 2000).”
Creativity and Discovery as Blind Variation: Campbell's (1960) BVSR Model after the Half-Century Mark (2011, background): “Th is a rti cl e is in te nd ed so le ly fo r t he p er so na l u se o f t he in di vi du al u se r a nd is n ot to b e di ss em in at ed b ro ad ly . algorithms can be referred to as “strong” methods, whereas heuristics represent “weak” methods (Klahr, 2000).”
The Promise and Potential of Big Data: A Case for Discovery Informatics (2014, background): “…including mechanisms for decomposing tasks, assigning tasks to and incentivizing participants, sharing relevant “mental models,” combining results, and at least in some domains, engaging large numbers of participants with varying levels of expertise and ability in discovery (“citizen science”).”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, result): “These results confirmed earlier findings that students have trouble using simulations productively and are unable to systematically manipulate different combinations of parameters, observe what happens, and record the results (Klahr 2002).”