Exploring students’ patterns of reasoning

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dc.contributor.author Matloob Haghanikar, Mojgan
dc.date.accessioned 2012-04-25T18:52:56Z
dc.date.available 2012-04-25T18:52:56Z
dc.date.issued 2012-04-25
dc.identifier.uri http://hdl.handle.net/2097/13646
dc.description.abstract As part of a collaborative study of the science preparation of elementary school teachers, we investigated the quality of students’ reasoning and explored the relationship between sophistication of reasoning and the degree to which the courses were considered inquiry oriented. To probe students’ reasoning, we developed open-ended written content questions with the distinguishing feature of applying recently learned concepts in a new context. We devised a protocol for developing written content questions that provided a common structure for probing and classifying students’ sophistication level of reasoning. In designing our protocol, we considered several distinct criteria, and classified students’ responses based on their performance for each criterion. First, we classified concepts into three types: Descriptive, Hypothetical, and Theoretical and categorized the abstraction levels of the responses in terms of the types of concepts and the inter-relationship between the concepts. Second, we devised a rubric based on Bloom’s revised taxonomy with seven traits (both knowledge types and cognitive processes) and a defined set of criteria to evaluate each trait. Along with analyzing students’ reasoning, we visited universities and observed the courses in which the students were enrolled. We used the Reformed Teaching Observation Protocol (RTOP) to rank the courses with respect to characteristics that are valued for the inquiry courses. We conducted logistic regression for a sample of 18 courses with about 900 students and reported the results for performing logistic regression to estimate the relationship between traits of reasoning and RTOP score. In addition, we analyzed conceptual structure of students’ responses, based on conceptual classification schemes, and clustered students’ responses into six categories. We derived regression model, to estimate the relationship between the sophistication of the categories of conceptual structure and RTOP scores. However, the outcome variable with six categories required a more complicated regression model, known as multinomial logistic regression, generalized from binary logistic regression. With the large amount of collected data, we found that the likelihood of the higher cognitive processes were in favor of classes with higher measures on inquiry. However, the usage of more abstract concepts with higher order conceptual structures was less prevalent in higher RTOP courses. en_US
dc.description.sponsorship National Science Foundation en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Reasoning en_US
dc.subject Rubric en_US
dc.subject Reform en_US
dc.subject Inquiry teaching en_US
dc.subject RTOP en_US
dc.subject reformed teaching observation protocol en_US
dc.subject logisitic regression en_US
dc.subject Bloom's revised taxonomy en_US
dc.subject National study of undergraduate science en_US
dc.subject transfer of learning en_US
dc.subject conceptual strcuture en_US
dc.subject backward design en_US
dc.subject Multinomial logistic regression en_US
dc.subject binary logistic regression en_US
dc.title Exploring students’ patterns of reasoning en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Physics en_US
dc.description.advisor Dean Zollman en_US
dc.subject.umi Physics (0605) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth May en_US

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