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Assessing causal mechanistic reasoning: promoting system thinking

dc.creatorWeinberg, Paul Jason
dc.description.abstractAbstract This dissertation study focuses on children’s development of causal mechanistic reasoning (Machamer, Darden, & Craver, 2000; Russ, Scherr, Hammer, & Mikeska, 2009; Shultz, 1982) in the area of simple mechanics. Reasoning about mechanism is critical to disciplined inquiry in science and engineering. This study focuses on a content domain within this area (i.e., reasoning about the motion of simple levered machines). Much extant literature addresses the cognitive resources children bring to situations involving topics like these. However, ironically, research also shows that older children (high school aged) and adults have great difficulty reasoning within these same domains. There is little research that addresses how this form of students’ scientific reasoning changes from the time they enter elementary school to high school and adulthood. This dissertation describes the development of an assessment instrument that diagnoses individuals’ mechanistic reasoning about the motion of simple levered machines. This assessment allows for the characterization and examination of different forms of reasoning that participants used to explain the motion of the levered machines. Elements of participants’ causal mechanistic reasoning are characterized. The participants included elementary, middle, and high school students as well as college undergraduates and adults without any college education. The vast majority (89%) of participants responded to the assessment items by diagnosing at least one machine mechanism as they predicted machine motion, suggesting that people in all the participant age groups have sufficient resources to engage in causal mechanistic reasoning about these systems. The results of IRT item analysis show that item difficulty depends on characteristics of the machines being diagnosed (e.g., number of levers, lever type, and the presence of intermediate levers). In addition, IRT modeling show that there is a difference between those participants who can diagnose all of a machine’s mechanistic elements and those who can both diagnose and causally connect them from input to output. The capacity to both diagnose and causally trace through the components of mechanical systems is important in STEM (science, technology, engineering, and mathematics) disciplines; it is also important in how individuals make sense of the designed world. Finally, this study addresses the stability of mechanistic reasoning by looking at the extent to which it is either consistently applied or, alternatively, disrupted by various machine characteristics.
dc.subjectThe Learning Sciences
dc.subjectScience Education
dc.subjectEngineering Education
dc.subjectSTEM Education
dc.subjectAssessment Design
dc.titleAssessing causal mechanistic reasoning: promoting system thinking
dc.contributor.committeeMemberRichard Lehrer
dc.contributor.committeeMemberRogers Hall
dc.contributor.committeeMemberSun-Joo Cho
dc.contributor.committeeMemberPhillip Crooke
dc.type.materialtext and Learning University
dc.contributor.committeeChairLeona Schauble

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