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Teaching Methods

  • May 12, 2009 Teaching and Learning hour-long webinar panel discussion presented by Laura Kubatko, The Ohio State University; Danny Kaplan, Macalester College; and Jeff Knisley, East Tennessee State University, and hosted by Jackie Miller, The Ohio State University. National reports such as Bio2010 have called for drastic improvements in the quantitative education that biology students receive. The three panelists are involved in three differently structured integrative programs aimed to give biology students the statistics that are useful in learning and doing biology. The three programs have some surprising things in common for teaching introductory statistics. All three involve connecting calculus and statistics. All three reach beyond the mathematical topics usually encountered in intro statistics in important ways. All three aim to keep the mathematics and statistics strongly connected to biology. The panelists describe their different approaches to teaching statistics for biology and discuss how and why an integrated approach gives advantages. Important issues are how to tie statistics advantageously with calculus, how to keep "advanced" mathematical and statistical topics accessible to introductory-level biology students, and how to employ computation productively. The discussion contrasts a comprehensive "team" approach (at ETSU) with stand-alone courses (at Macalester and at OSU) and refers to the institutional opportunities and constraints that have shaped the programs at their different institutions.

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  • Webinar presented by Roger Woodard of North Carolina State University and Ginger Rowell of Middle Tennessee State University and hosted by Jackie Miller of The Ohio State University on June 13, 2006. Many people would like to use online resources in their classrooms. However, the typical online applet does not have supporting materials that allow the teacher to introduce them into the classroom. Instructors that simply point students to a website without specific instructions and planning may find that the students do not achieve the desired learning outcomes from using the applet. In this webinar Dr. Woodard presented a basic framework that instructors can use to plan and implement the use of online materials in the classroom. These are illustrated with examples that have been field tested in courses at NCSU and at MTSU.
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  • Funded by the National Science Foundation, workshops were held over a three-year period, each with about twenty participants nearly equally divided between mathematics educators and statisticians. In these exchanges the mathematics educators presented honest assessments of the status of mathematics education research (both its strengths and its weaknesses), and the statisticians provided insights into modern statistical methods that could be more widely used in such research. The discussions led to an outline of guidelines for evaluating and reporting mathematics education research, which were molded into the current report. The purpose of the reporting guidelines is to foster the development of a stronger foundation of research in mathematics education, one that will be scientific, cumulative, interconnected, and intertwined with teaching practice. The guidelines are built around a model involving five key components of a high-quality research program: generating ideas, framing those ideas in a research setting, examining the research questions in small studies, generalizing the results in larger and more refined studies, and extending the results over time and location. Any single research project may have only one or two of these components, but such projects should link to others so that a viable research program that will be interconnected and cumulative can be identified and used to effect improvements in both teaching practice and future research. The guidelines provide details that are essential for these linkages to occur. Three appendices provide background material dealing with (a) a model for research in mathematics education in light of a medical model for clinical trials; (b) technical issues of measurement, unit of randomization, experiments vs. observations, and gain scores as they relate to scientifically based research; and (c) critical areas for cooperation between statistics and mathematics education research, including qualitative vs. quantitative research, educating graduate students and keeping mathematics education faculty current in education research, statistics practices and methodologies, and building partnerships and collaboratives.
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  • The Numbers Guy examines numbers in the news, business and politics. Some numbers are flat-out wrong or biased, while others are valid and help us make informed decisions. Carl Bialik tells the stories behind the stats, in daily updates on this blog and in his column published every other Friday in The Wall Street Journal.
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  • Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.
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  • This website is a resource of teaching methods and approaches that instructors at all levels of statistics education can use to generate student interest in pursuing more study or a career in the field of statistics.
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  • This website's purpose it to promote the improvement of statistical education, training and understanding. You will find activities, resources and current news to help in teaching statistics.
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  • This case study discussess methods to successfully adapt graduate-level statistics courses for the online environment. Using small-group discussion assignments is not only a great way to create an interactive learning community; it also provides instructors with valuable information about students' reasoning.
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  • This page calculates either estimates of sample size or power for differences in proportions. The program allows for unequal sample size allocation between the two groups.

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  • The following exercise can illustrate the problem of bias in estimators to students in statistics courses. In some advanced courses an alternative estimator may be presented and properties of this estimator may be investigated via Monte Carlo studies.
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