INSPIRING STUDENTS TO UNLEASH SIMPLE TECHNOLOGICAL TOOLS TO PROVIDE BETTER DATA ANALYSIS TO DECISION MAKERS

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In an era of technological advances, the proliferation of large-scale datasets is both an asset and a challenge for decision makers. In educating future decision makers and analysts, it is difficult to recreate this paradox in the classroom. First, student ability in managing large-scale, realistic datasets is lacking. Students self-assess as being comfortable with technology in general, but lack the skills required to handle large quantities of data. Second, it is difficult to adapt large-scale and sometimes problematic datasets encountered in practice to exercises suitable for undergraduates. We describe an approach to teaching decision-making with large-scale datasets that involves a problem-based learning model derived from the field experiences of our faculty who regularly deploy as United States Army operations research analysts. This process involves our faculty returning from assignments as analysts in Iraq and Afghanistan and immediately adapting realistic datasets to classroom problems the very next semester. The approach emphasizes creation of Simple Technological Tools (STT) which are automated tools students build using advanced features of standard office software. This approach has been very successful in focusing the skill development of our students, motivating learning, and promoting an element of realism in our teaching of data analysis to support decision-making.