A Comparison Of Traditional And Active Learning Methods: An Empirical Investigation Utilizing A Linear Mixed Model

dc.contributorWeltman, Daviden_US
dc.date.accessioned2008-04-22T02:41:21Z
dc.date.accessioned2011-08-24T21:41:11Z
dc.date.available2008-04-22T02:41:21Z
dc.date.available2011-08-24T21:41:11Z
dc.date.issued2008-04-22T02:41:21Z
dc.date.submittedNovember 2007en_US
dc.description.abstractThis research aims to understand what types of learners (business school students) benefit most and what type of learners may not benefit at all from active learning methods. It is hypothesized that different types of students will achieve different levels of proficiency based on the teaching method. Several types of student characteristics are analyzed: grade point average, learning style, age, gender, and ethnicity. Three topics (in the introductory business statistics course) and five instructors covering seven class sections are used with three different experimental teaching methods. Method topic combinations are randomly assigned to class sections so that each student in every class section is exposed to all three experimental teaching methods. A linear mixed model is utilized in the analysis. The effect of method on student score was not consistent across grade point averages. Performance of students at three different grade point average levels (high, middle, low) tended to converge around the overall mean when learning was obtained in an active learning environment. Student performance was significantly higher in a traditional method (versus an active learning method) of teaching for students with high and mid-level grade point averages. The effects of the teaching method on score did not depend on other student characteristics analyzed (i.e. gender, learning style or ethnicity).en_US
dc.identifier.urihttp://hdl.handle.net/10106/734
dc.language.isoENen_US
dc.publisherInformation Systems & Operations Managementen_US
dc.titleA Comparison Of Traditional And Active Learning Methods: An Empirical Investigation Utilizing A Linear Mixed Modelen_US
dc.typePh.D.en_US

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