Using K-16 Curricular Materials in Cognitive Psychology Experiments

• In Event: 2-159 - Applications of Cognitive Science Research: Contributions to Education Across Levels of Analysis

Integrative Statement

One barrier to translating cognitive science research into usable knowledge for education has been the distance between experimental paradigms and stimuli and real-world, academically relevant problems. Using academically relevant problems as stimuli in rigorous, theoretically grounded cognitive science research is one way to increase the likelihood that findings will be translatable. I will focus in particular on the potential applications of relational category learning research to real educational problems.
Many science, technology, engineering, and mathematics (STEM) concepts have a relational underlying structure, in the sense that they involve understanding the relationships between elements, rather than being defined by perceptual or semantic features (Alexander et al., 2016; Day & Goldstone, 2012; Gentner, Loewenstein, & Thompson, 2003; Richland, Stigler, & Holyoak, 2012). For this reason, cognitive science research on how relational concepts are acquired and represented can be informative for optimizing instructional strategies and materials for K-16 STEM education. Considering relational concepts as relational categories allows many of the methods and models of (feature-based) category learning to be applied to relational concepts (Goldwater & Schalk, 2016). There is growing evidence to suggest that relational categories can be learned via the many of the same methods as feature-based categories. So far, this approach has been used with simple abstract concepts such as “monotonic” or “symmetrical” as categories, but has not been used with quantitative concepts or concepts that are readily applicable to STEM content. For example, many category learning experiments are structured as a simple form of supervised learning in which participants categorize stimuli one at a time (at first with little or no guidance on how to do so) and receive feedback on each trial. Several studies have now demonstrated that people are in fact able to learn relational categories this way despite the critical differences between feature-based and relational categories (Goldwater, Don, Krusche, & Livesey, 2018; Reber, Luechinger, Boesiger, & Henke, 2014). This approach also allows for the possibility of optimizing instruction by strategically selecting and sequencing examples from the problem space—choices that have been shown to have consequences for the learning and transfer of both feature-based and relational categories (Carvalho & Goldstone, 2015; Eaves Jr & Shafto, 2014; Mathy & Feldman, 2016; Shafto, Goodman, & Griffiths, 2014) (Braithwaite & Goldstone, 2012; Lindsey, Mozer, Huggins, & Pashler, 2013). However, relational category learning paradigms are just beginning to be applied to authentic STEM-domain content (Nosofsky, Sanders, Zhu, & McDaniel, 2018; Sen et al., 2018).
I will present examples of learning objectives from the Common Core Curriculum and/or commonly used textbooks and how they could be adapted for use in basic cognitive research on learning, memory, and transfer. I will then present sample experimental designs using these materials and address issues of validity and reliability that arise when using authentic educational content materials rather than artificial stimuli. Artificial stimuli are often preferred because the stimulus space can be carefully controlled, and because participants bring no prior knowledge of the domain. However, these issues can be mitigated when using non-artificial stimuli.

Author

• Priya Kalra, University of Wisconsin-Madison
  Presenting Author