Technology-Enhanced Statistics Education with SOCR
Chaojie Zhou, University of California, Los Angeles, United States
University of California, Los Angeles . Awarded
There is an ongoing need for clear and accessible statistics teaching tools for both learners and instructors. Applications, step by step tutorials, and visualizations are extremely useful tools for teaching students to think scientifically, properly analyze the data, use proper techniques, and identify common errors. In this paper we will demonstrate technology-enhanced approaches for introductory statistics courses. Specifically we develop two different activities, using SOCR (Statistics Online Computational Re- source) data, tools and resources. The first activity introduces multiple linear regression using appropriate SOCR tools. In general, linear regression is used to describe a relationship between one variable to one or several other variables. Linear regression is used extensively in practical applications such as prediction and measuring the strength of relationships between variables. Proper linear regression techniques will be demonstrated, and appropriate methods for the analysis of regression results will be discussed. The second activity demonstrates the interactive power of the SOCR Motion Charts tool. SOCR Motion Charts allow the visualization of multivariate and high-dimensional data that has time and location dimensions. Used correctly, data visualization and statistical graphics are useful in presenting data in clear, intuitive, and engaging ways. Proper data visualization can reveal patterns and relationships that would have been hidden in other data structures, such as tables. The SOCR Motion Charts tool allows us to represent variables based on their size, time, and location attributes. With this technology we can detect patterns across time, as well as analyze the relationships of variables in terms of their magnitudes and locations. These activities and tutorials are implemented as interactive hands-on learning materials and are openly accessible on the web through the SOCR site www.socr.ucla.edu/.
Zhou, C. Technology-Enhanced Statistics Education with SOCR. Master's thesis, University of California, Los Angeles.
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