(206c) Identifying and Repairing Students' Misconceptions in Thermal and Transport Science

This paper presents an integration of two research lines combining identification of students' misconceptions of difficult engineering concepts with efforts to repair such misconceptions. Previous studies reported that misconceptions related to heat transfer, fluid mechanics, thermodynamics, and other engineering and science concepts persist among engineering students even after they completed college-level courses in the subjects. Therefore, the first line of our research was focused on two research questions: ?What important concepts in thermal and transport science are difficult for engineering students to learn?? and ?How an instrument can be developed to identify engineering student misconceptions of these difficult and important concepts?? The first research question was investigated by conducting a Delphi study with experienced engineering faculty to identify important and difficult concepts in thermal and transport science. The second research question was investigated by developing the Thermal and Transport Concept Inventory (TTCI). The TTCI is an instrument that measures the conceptual understanding of key ideas in thermodynamics, fluid mechanics, and heat transfer for undergraduate engineering students. Thus, TTCI is also a tool for identifying students' misconceptions in thermal and transport science.

This presentation will discuss the methodology used to identify important and difficult thermal and transport concepts which were included in the TTCI. It also discusses the methodology and procedures that generate TTCI items developed using student responses to open-ended questions followed by beta testing of multiple-choice items. Student responses were used to create plausible but wrong answers for each item. Beta test data from six engineering schools were used to calculate traditional measures of reliability. The validated TTCI will be helpful for instructors to indentify students' miscomputations of key concepts in thermal and transport science and to adapt their teaching and instruction according to students' knowledge level of the subjects. This line of research also provides a set of methods and procedures to develop and validate other concept inventories.

The second line of our research was focused on how to repair students' misconceptions of difficult engineering concepts, which is a continuity of the first line of research. Due to the robust nature of such misconceptions, traditional instructional strategies are not effective to correct or repair them. Thus, for this line of the research, we investigates the development and testing of schema training strategies for helping engineering students develop more fundamentally accurate mental models for difficult engineering and science concepts.

The schema training strategies were based on the assumption that students learn new concepts by assimilating or encoding new information into an existing schema or category. Assimilation helps students make inferences about and assign attributes to a new concept or phenomenon. However, when students learn some particularly challenging engineering concepts or processes, which are fundamentally different from their commonsense or observable conceptions, they can make the wrong inference or assign incorrect attributes to the new concepts based on their existing incomplete or incorrect schema. A simple everyday example of this is that some students think that a whale is a fish. If these incorrect inferences and attributes are not corrected, they could hinder correct understandings and be reinforced as students take more course work in the subjects. That means the misconceptions become more robust and resistant to traditional instruction.

To repair such robust misconceptions, Chi and her colleagues proposed an innovative instructional approach involving schema training methods which focus on helping students develop appropriate schemas or conceptual frameworks for learning difficult and challenging engineering concepts. Such methods were effective in helping middle school students and undergraduate psychology students learn difficult science concepts. We are testing Chi's theoretical framework by creating effective schema training protocols and materials that help engineering students create appropriate mental models of fundamentally important dynamic processes and concepts, especially those operating at small length scales.

There is ample evidence in the literature to suggest that students of all ages (including science and engineering students) do not easily understand fundamental small-scale phenomena such as heat transfer, diffusion, fluid mechanics, and electricity. Given the current interest in advances in nanotechnology (e.g.microfluidics, biotechnology, genetic engineering, nanoscale machines), new engineering graduates must have a firm grip of fundamental processes which are characterized by small-scale dynamic systems. Therefore, schema training methods hold promise not only for thermal and transport science but also for other disciplines in engineering. Available schema training results comparing an experimental group of engineering students with a control group will be presented and discussed in this paper.