Date of Award
Doctor of Philosophy
Science Education, Mallinson Institute
Dr. Heather Petcovic
Dr. Todd Ellis
Dr. Nicole LaDue
Dr. David Rudge
To see the world as a meteorologist, one must understand and interpret atmospheric processes through representations depicted on two-dimensional weather charts and maps that encode large amounts of spatial and numerical data. This is a cognitively demanding and spatially challenging task, especially for students with burgeoning levels of meteorology knowledge, who lack the expertise of practiced meteorologists that read such charts and maps with ease. With little prior work informing meteorology and meteorology education through a cognitive science lens, this study surveys the literature and follows models of discipline-based education and cognitive science research to identify the discrete intelligence factors contributing to the successful completion of a series of meteorology tasks. The overall purpose of the study is to identify malleable and stable intelligence factors that predict performance on the meteorology task series and characterize them for the purpose of designing instructional interventions and scaffolding student learning in undergraduate meteorology courses. Framed by Cattell-Horn-Carroll theory, and using a mixed-methods, embedded correlational design informed by exploratory research, this study follows a three-phase progression of inquiry that is described in three independent manuscripts. The first manuscript reports survey research that identifies the spatial thinking skills that student and professional meteorologists (N = 93) report using in their work. By identifying mental animation, disembedding and perspective taking as spatial skills likely influential in meteorology, this initial study effectively taps into the knowledge and wisdom of practitioners, and lays the groundwork for further investigation. The second manuscript describes a quantitative investigation that expands beyond the spatial thinking skills identified in the first study, to include other intelligence factors, including working memory, domain knowledge and expertise. In sum, five discrete intelligence abilities are measured among meteorologists (N= 81) representing a range of expertise to determine their effect on performance on the meteorology task series. Ultimately, domain knowledge and disembedding emerge as significant predictors of meteorology skill, thus highlighting the importance of meteorology content coupled with good pattern identification ability as critical to success. The third manuscript describes a qualitative study using verbal data resulting from the think-aloud processes of a subset of meteorologists and meteorology students who participated in the prior study. This manuscript describes an in-depth analysis of three of these participants (n = 3) who exemplify low, medium and high disembedding skills. Evidence of the use of disembedding while working through the meteorology task series is quantified and characterized on a continuum representing low to high disembedding skills. This characterization exhibits the use of rule-based reasoning to augment disembedding, and highlights the aforementioned significance of knowledge and pattern identification when completing meteorology tasks. Overall study results are discussed in the context of meteorology education along with evidence-based potential directions for meteorology instruction.
McNeal, Peggy M., "ldentifying and Characterizing Cognitive Factors Significant to Practicing and Learning Meteorology" (2017). Dissertations. 3192.