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This study investigates undergraduate STEM students’ interpretation of quantities and quantitative relationships on graphical representations in biology (population growth) and chemistry (titration) contexts. Interviews (n = 15) were conducted to explore the interplay between students’ covariational reasoning skills and their use of disciplinary knowledge to form mental images during graphical interpretation.

Our findings suggest that disciplinary knowledge plays an important role in students’ ability to interpret scientific graphs. Interviews revealed that using disciplinary knowledge to form mental images of represented quantities may enhance students’ covariational reasoning abilities, while lacking it may hinder more sophisticated covariational reasoning. Detailed descriptions of four students representing contrasting cases are analyzed, showing how mental imagery supports richer graphic sense-making.

In the cases examined here, students who have a deep understanding of the disciplinary concepts behind the graphs are better able to make accurate interpretations and predictions. These findings have implications for science education, as they suggest instructors should focus on helping students to develop a deep understanding of disciplinary knowledge in order to improve their ability to interpret scientific graphs.

 

Genetics plays an increasing role in modern life as evidenced by the development of revolutionary techniques such as CRISPR-based genome editing and the rise of personalized genome services. However, genetics is difficult to learn; known issues include its abstract nature, different scales, and technical language. Pedigree analysis is a convergence of these concepts, requiring use of multiple symbolic scales and understanding the relationships and nature of alleles, genes, and chromosomes. To measure student understanding of these concepts, as well as support biology educational reform toward student-centered instruction, we developed a formative assessment to provide reliable and valid evidence of student understanding, learning, and misconceptions for pedigree analysis. Nine multiple choice items targeted to four learning objectives were developed in an iterative process with faculty and student input. We designed distractor answers to capture common student misconceptions and deployed a novel statistical technique to assess the congruence of distractor language with targeted misconceptions. Psychometric analysis showed the instrument provides valid and reliable data and has utility to measure normalized learning gains. Finally, we employed cross-tabulation and distractor progression to identify several stable misconceptions that can be targeted for instructional intervention. 

There is currently a severe shortage of teachers in the U.S. workforce. The problem is especially acute among science, technology, engineering, and mathematics (STEM) teachers and exacerbated by high turnover among new teachers—those with less than 5 years of teaching experience. In this article, the authors investigate one piece of the puzzle. The authors model a social cognitive approach to understanding self-efficacy, a key precursor to job performance and retention. Their interactionist approach accounts for both demographic (i.e., gender and age) and relational variables (i.e., social networks). The authors test their ideas on a sample of 159 STEM teachers across five geographic regions in the United States. Their analysis reveals patterned differences in self-efficacy across gender that are contingent on the communities of practice in which the teachers are embedded. Together, their theory and findings highlight the value of taking a holistic, interactionist view in explaining STEM teacher self-efficacy.