| NSF Org: |
DUE Division Of Undergraduate Education |
| Recipient: |
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| Initial Amendment Date: | April 21, 2017 |
| Latest Amendment Date: | January 16, 2018 |
| Award Number: | 1712475 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Mike Ferrara
mferrara@nsf.gov (703)292-2635 DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
| Start Date: | June 1, 2017 |
| End Date: | May 31, 2022 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
733 BROAD ST GRINNELL IA US 50112-2227 (641)269-4983 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
733 Broad Street Grinnell IA US 50112-2227 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | IUSE |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The objective of our NSF IUSE: Exploration and Design Tier for Engaged Student Learning proposal was to advance STEM learning by creating, implementing, and testing inquiry-based online games that simulate data-based decision-making embedded in a research-like experience. Instead of teaching statistics and data science as a collection of facts and mathematical calculations, these activities excite the power of innovation and creativity that occurs within great research in any discipline. While games are engaging, the key motivation for our game-based labs is that they train students to make better data-based decisions. Each activity allows students to develop their own research questions, collect their own unique data, easily visualize their data through interactive apps, and then make meaningful conclusions. In addition, videos or research articles are typically assigned with each activity to encourage students to understand the challenges involved in drawing conclusions from data.
Textbook data sets provided to students are typically carefully vetted and cleaned in order to illustrate a key statistical topic or method. Rarely are real studies and data so straightforward. We build upon educational research from multiple disciplines by creating modules that bridge the gap from smaller, focused textbook problems to real-world problems. The result is that core statistical issues such as working with messy data, bias, data relevance, and reliability are taught as concepts essential to every study involving data.
Each game is carefully designed to be highly adaptable and can be incorporated into a wide variety of courses. Class testing has shown that these game-based labs offer several advantages in quantitative undergraduate courses. While students enjoy the unusual experience of using a game for learning statistics, most students identified that the best part of these game-based labs was that they helped them learn. Many students are engaged in a unique way and are enthusiastic about the opportunity to pose their own research questions and control their own data. Students also are demonstrating interest in going beyond the required coursework and spending their own time learning more advanced techniques through these materials.
To make the materials easily accessible to any type of undergraduate course we have created several websites. The set of games, with student data, and corresponding interactive data visualizations are freely available at https://stat2games.sites.grinnell.edu/. In addition, instructor resources are on our Stat2labs website, http://web.grinnell.edu/individuals/kuipers/stat2labs.
Last Modified: 09/29/2022
Modified by: Shonda R Kuiper
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