BIRI enables researchers who wish to study the effectiveness of their intervention approach to support students in higher education at scale. The intervention can be delivered anywhere in the course content that is hosted on the Realizeit platform.
The intervention format is flexible: an intervention can consist of text (e.g., study tips, stories, testimonials), images (e.g., infographics, photos), videos (e.g., a tutorial), and writing activities (e.g., to write about study goals, utility value, or belonging), as long as the intervention content can be placed in one or two stand-alone modules that are independent of the rest of the course content.
Our team aims to create a cross-disciplinary research environment that brings together scientists in education, psychology, computer and information science, behavioral economics, and the behavioral and computational social sciences. BIRI will enable robust, open, and iterative research by facilitating intervention implementation, data collection, and study replication. This will dramatically reduce the time it takes for intervention researchers to conduct research while increasing the number of researchers who have access to experimentation sites in education.
For researchers, BIRI uses the following steps for each proposed intervention project:
- Researchers need to submit a study proposal that describes the intervention and specifies the intended sample size, student population, or course subject
- We will match researchers with relevant courses based on the information in the proposal and reach out to researchers prior to the start of a coming semester
- We will add the proposed intervention content as a randomized controlled trial into one or more matched courses
(Optional: Researchers can add additional questions to the standardized course survey) - We will run the intervention through the semester
(Optional: Researchers can replicate their intervention in new semesters) - We will provide de-identified data to researchers. The data include unit-level affect (focused, anxious, bored, confused, frustrated), survey (e.g., belonging uncertainty, confidence about performance, etc.), behavioral, performance data (unit-level assignment grades on Realizeit)
Researchers are encouraged to pre-register their studies before they run the intervention and receive the de-identified data.
Affect detectors are being developed and validated for Realizeit courses within this project, and will provide fine-grained data on this aspect of the student experience.