Models of Data: A Theory of How People Evaluate Data

By Clark A. Chinn, William F. Brewer. 2001

170 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Family Practice

View Paper: https://www.semanticscholar.org/paper/80f2e0ac2c1d0ef15764127bead7bf4f337d0d6b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Deanna Kuhn
2. Corinne Zimmerman
3. Merce Garcia-Mila
4. E. Michael Nussbaum
5. David Dean, Jr.
6. Erik Blair
7. Katherine L. McNeill
8. C. Andersen

In Cognitive Neuroscience

1. A. Baron
2. Merce Garcia-Mila
3. Deanna Kuhn
4. E. Michael Nussbaum
5. David Dean, Jr.
6. Erik Blair
7. Katherine L. McNeill
8. Corinne Zimmerman

In Family Practice

1. Deanna Kuhn
2. Erik Blair
3. Katherine L. McNeill
4. Corinne Zimmerman

Citations in the corpus

• Plausibility Judgments in Conceptual Change and Epistemic Cognition (2016, background): “Chinn and Brewer (2001) have found that, on a psychological level, comprehension of a physical mechanism results in greater plausibility.”
• The Development of Scientific Thinking Skills in Elementary and Middle School. (2007, methodology): “Chinn and Brewer (2001) suggest that the cognitive model is then evaluated by considering the plausibility of these links.”
• Cognitive Foundations of Learning Argumentation (2007, background): “Our own work (Kuhn et al., 1992, 1995) as well as others’ (Cheng & Novick, 1992; Chinn & Brewer, 2001; Klaczynski, 2000; Klahr, 2000) show how easy it is for the adolescent and even the adult to ignore evidence that contradicts their expectations or to interpret it either partially, recognizing on”
• A reflexive exploration of two qualitative data coding techniques (2015, background): “Chinn and Brewer (2001) suggest that when people analyse data they "construct a cognitive model of the data according to the perspective of the person who is reporting the data" (p.”
• Supporting Students' Construction of Scientific Explanations by Fading Scaffolds in Instructional Materials (2006, background): “Students are more likely to discount data if the data contradicts their current theory (Chinn & Brewer, 2001), and they are more likely to consider data if they can come up with a mechanism for the pattern of data (Koslowski, 1996).”
• Science Is Awe-Some: The Emotional Antecedents of Science Learning (2017, background): “…of expectations, while violations that represent major threats to understanding either evoke fear-like aversive reactions (Hebb, 1949; Loewenstein, 1994) or are simply ignored because of an inability to assimilate the new information into existing mental structures (Chinn & Brewer, 2001).”
• Urban High School Students’ Critical Science Agency: Conceptual Understandings and Environmental Actions Around Climate Change (2012, background): “Individuals evaluate data based on their prior conceptions and theories (Chinn and Brewer 2001).”
• Connecting Scientific Reasoning and Causal Inference (2004, background): “One exception is an article by Chinn and Brewer (2001) that cited articles from both literatures, although it did not undertake to connect them.”