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Student Engagement in Statistics Using Technology: Making Data Based Decisions: 1712475

Principal Investigator: Shonda Kuiper
CoPrincipal Investigator(s): Rodney Sturdivant
Organization: Grinnell College

There are ongoing national needs on two important fronts for improvements in undergraduate statistics and data science education. There is a need for United States residents to establish a deeper understanding of these arenas in order to make informed decisions in an increasingly diverse and complex society. Likewise, there is a fundamental need for the country to produce college graduates in science, technology, engineering, and mathematics (STEM) areas who can apply statistics and data science to help provide the Nation with a globally competitive STEM workforce. The investigators on this project will address these needs by designing, developing and evaluating inquiry-based online technology that will simulate current real-world scenarios in statistics and data science to connect students to the importance of the investigative process of problem-solving and data-based decision making and to the skills needed for these activities. In connection with this, the project will take advantage of large, publically available datasets which are now easily accessible to engage students with research-like experiences and technologically interactive educational materials to foster students’ abilities in understanding and applying statistics and data science. In addition to materials for students, resources to be developed will be specifically designed to help instructors incorporate key ideas typically not taught in traditional textbooks, such as interactive visualizations, working with messy data, bias, data relevance, reliability, and full research-like experiences involving real-world data.

A key aim of the project is to advance STEM learning through the creation, implementation, and testing of inquiry-based, interactive, online investigative labs that will simulate data-based decision making. Goals of the project include: (1) incorporating research-like experiences for students into their studies; (2) addressing the increased importance of data science and challenging statistical concepts not easily addressed in current courses and textbooks; (3) developing full story line models of real-world scenarios using game-like simulations; and (4) creating and vetting materials that can be incorporated into a variety of traditional introductory and advanced undergraduate courses. The project team’s theory of action is built on developing, implementing and assessing each simulation lab and other components of the project according to these goals. An important motivation is to allow students to develop their own research questions, generate and use their own unique data to make decisions, and then observe and learn from the choices made through their interactions with the technology. To evaluate success of the approaches taken, the project will employ a mixed methods approach to compare learning gains from more traditional materials to the gains made with these new materials, evaluate student attitudes and engagement, use data analytics to assess the effectiveness of the components embedded in each lab, and determine best practices for incorporating the resources into a variety of traditional introductory and advanced undergraduate courses. These fully immersive online game-like labs will be significantly different from other current textbook and online sets of educational materials as each lab will include inquiry-based case studies that contain real-world data analysis complexities, thereby providing a solid introduction to the intellectual content and broad applicability of statistics and data science. In concert with the software lab development, the team will create and vet a new student assessment tool that will be implemented to evaluate students’ abilities related to understanding of conceptual connections, communication skills, critical thinking, and problem-solving, as well as their ability to understand and work through challenges emanating from real-world, unstructured datasets. Overall, this project will have significant impact in STEM education by stimulating the power of innovation, creativity, and excitement that occurs within STEM research and applications.

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