Integrating Data Science into Undergraduate Psychology Education: A Capacity-Building Workshop for Undergraduate Psychology Students

This project aims to serve the national interest by providing training in data science to undergraduate attendees at the Southwestern Psychological Association (SWPA) Conference. Attendees will come from two- and four-year institutions of higher education, and up to twenty five students will receive funding to take part. The purpose of the project is twofold: 1) to provide a data science education undergraduate-level workshop at the SWPA annual meeting, and 2) to increase diversity in data science by equipping diverse psychology students with data science skills.

The primary focus of the undergraduate educational workshop will be to build participants’ knowledge of data science and increase their ability to employ data science methods in research. Skills that will be reviewed in the training include acquiring, visualizing, and managing data and performing specialized analyses. Using readily accessible, social media data (e.g., Twitter or Facebook) with broad applicability across interests, textual nature, and amenability to network analysis, workshop instructors will guide attendees through a high-level look at the process of developing a research proposal that utilizes big data.

Additionally, attendees will learn how the study of psychology and data science can be mutually beneficial, and also gain exposure to career paths and options from a panel discussion involving data scientists.

Enhancing psychology undergraduate training via data science education would provide an opportunity to examine and address validity and fairness issues in coding of natural language and algorithm training/validation. Natural language processing (NLP) is one of the fastest growing areas of machine learning research. Current linguistic machine learning models do an adequate job on language-understanding tasks, but the patterns learned in the data have been shown to produce algorithms that often express stereotypes and social biases.

Stereotypes, prejudice, and implicit bias are a major focus of social psychological curriculum and training. If psychology education not only included theories and concepts surrounding these topics but also data science methodology, psychology students could conduct research that would assist in the optimization of machine learning models to reduce implicit biases.

Thus, the proposed conference workshop will demonstrate the relationship between STEM learning in formal and informal settings by providing a unique informal setting (conference workshop) where student learning takes place but has not been assessed or compared to learning that happens in a typical classroom setting. Through the informal learning environment of the conference this workshop will (a) provide context and purpose to formal learning, (b) provide students opportunity and access to data science professionals, and (c) extend STEM content learning environments and student engagement.

Participants knowledge development and attitude formation will be evaluated through direct measures of knowledge as well as qualitative methods. The findings from the assessment of this workshop will be developed into a mini-workbook and external-facing resource portal that will be shared broadly through the SWPA website and the American Psychological Association's educational directorates.

The mini-workbook will also be shared with other psychological regional organizations and APA leadership to develop similar workshops and training for annual meetings and national conferences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.