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Hands-on laboratory experience is a core element of introductory courses in most scientific disciplines, providing potential majors and non-majors alike opportunities to immediately experience applied practice. By contrast, many introductory courses in social sciences focus entirely on conceptual learning. Integrating hands-on data analysis can increase student engagement and provide a greater depth of understanding of key concepts, but it is challenging to combine non-trivial applied data analysis with the breadth of topical learning typically expected in introductory sociology courses, using quantiative statistics for examples but applicable to qualitative methods as well.
This workshop is targeted to instructors of introductory and non-major social science courses looking to enhance their teaching with applied data analysis. It is based on experience as a consultant, collaborator and guest instructor in data analytics across introductory courses in multiple departments, including many mistakes. Active practice of data analytic skills and statistical reasoning early in a program can provide the foundation for improved comprehension in later statistics and research methods courses, as well as increasing engagement and appeal of social science programs for potential future majors.
By the end of the workshop, participants will develop a preliminary plan for a data analysis activity in their own course. The workshop will be interactive and hands-on and presentation will focus on (a) identifying opportunities to integrate applied analytics with existing learning outcomes, (b) streamlining analytics activities to maintain focus on the course's primary objectives, and (c) creating learning objectives, reflection opportunities, and assessment plans that maximize the activity's value to learners and instructors.