Poster #1 - Learners as individual builders: Self-derivation as a tool to build knowledge and content mastery

• In Event: Poster Session 17

Abstract

Knowledge is the currency of academic and career achievement. Building a knowledge base is thus one of the most crucial tasks of development. The factual information that comprises knowledge is often thought to be gained directly from lessons or other educational experiences. However, considerable evidence reflects that productive process like analogical reasoning and deduction also are significant mechanisms of knowledge development (Goswami, 2011). Self-derivation of new factual knowledge through integration of separate yet related episodes is a productive process of particular interest for several reasons. Self-derivation results in new knowledge (Bauer, 2021) that is rapidly incorporated into the knowledge base (Bauer & Jackson, 2015), retained over time (Varga & Bauer, 2013), can be used for further self-derivation (Wilson & Bauer, 2021), and is related to academic achievement (Esposito & Bauer, 2022). Both age-related change and individual variability have been observed in self-derivation. Specifically, 4-, 6-, 8-, and 10- year-olds exhibit significant improvements in self-derivation performance between age groups (Bauer & Larkina, 2017; Esposito & Bauer, 2018). Furthermore, children’s self-derivation performance predicts end-of-year reading and math scores (Esposito & Bauer, 2022). Given the significance of self-derivation for knowledge base development and academic achievement in general, we are motivated to ask how self-derivation of new knowledge relate to accumulation of content in the domains of science, social studies, and humanities.
To further elucidate the pervasive, meaningful role of self-derivation in knowledge base development, we investigated self-derivation of new factual knowledge in a large sample of children between 8- and 12-years-old (n=165), tested in two sessions spaced one week apart. We also tested children’s accumulation of content knowledge in the specific domains of the humanities, social studies, and science. Children successfully self-derived on about 32% (SD = .21; range = .00-.80) of trials, consistent with previously observed levels of performance (Esposito & Bauer, 2018). Importantly, regression analyses make clear age did not significantly predict self-derivation. Rather, self-derivation was predicted by children’s memory for the individual stem facts used to derive new knowledge and accounted for 56% of the variance. Critically, in turn, children’s self-derivation and stem fact memory significantly predicted their performance on measures of social studies and science achievement but not humanities. In models with age as a covariate, self-derivation performance accounted for 18% and 15% of the variance in social studies and science achievement, respectively. Stem fact memory accounting for 22% and 26% of the variance in social studies and science achievement, respectively. These results provide two crucial insights. By the end of the first decade of life, variability in self-derivation appears to be due to individual rather than age-related differences (as is observed in younger children). Self-derivation contributes significantly to achievement in the important domains of science and social studies. Taken together, these results suggest self-derivation is truly a tool of building a knowledge base from individual facts to having command over content.

Authors

• Jessica Ann Dugan, Emory University
  Presenting Author

• Katherine Anne Lee, Emory University

• Melanie H. Hanft, Emory University

• Alissa G. Miller, Emory University

• Britney Del Solar

• Lucy Cronin-Golomb, Emory University

• Patricia J Bauer, Emory University