Tools and curricula involving games, models, and simulations engage students in rich investigations and open up many new possibilities for deep learning. Digital curricula also collect streams of data, which offer the potential to inform real-time interactions in the classroom. Intelligent tutoring systems can aggregate these data to develop complex models of students and their thinking, apply these models probabilistically to detectors and pedagogical decision maps, and develop intricate suggestions for further instruction.
Our goal is to improve student learning of genetics content by developing and researching a layered learner guidance system that aids students and informs student-student and student-teacher interactions. GUIDE is a hybrid system that partners an intelligent tutoring system (ITS) with the pedagogical expertise of the classroom teacher and existing classroom networks of peer support. Such a system can bring to bear the rich user models of ITS and leverage their aggregated knowledge of the class as a whole. As such, this system—teacher and students plus ITS—can act as a critical new guide for student learning support, expanding opportunities for assisting students effectively when they encounter problems, offering insights related to class-wide activity, strategizing for effective next steps, and permitting new exploration into how to enhance and deepen student learning.
We have partnered with North Carolina State University to develop GUIDE, and are implementing this system within Geniverse, a proven digital learning environment built to support high school genetics. Geniverse is a game-like environment intended for use in the classroom; the GUIDE system interfaces directly with Geniverse, using and processing student interactions with the software to inform interactions in the classroom.
We’re researching how an ITS-based learner guidance system can best expose information about student practices and conceptual understanding to improve support of student learning in the classroom context, and ultimately improve students’ knowledge and practices.
View these videos and more on the Concord Consortium YouTube Channel.
- Rachmatullah, A., Reichsman, F., Lord, T., Dorsey, C., Mott, B., Lester, J., & Wiebe, E. (2020). Modeling secondary students’ genetics learning in a game‑based environment: Integrating the expectancy‑value theory of achievement motivation and flow theory. Journal of Science Education and Technology.
- Horwitz, P., Lord, T., & Reichsman, F. (2020). Students learn genetics with geniventure. @Concord. 24(2).
- McElroy-Brown, K., & Reichsman, F. (2019). Genetics with dragons: Using an online learning environment to help students achieve a multilevel understanding of genetics. Science Scope, 42(8), 62–69.
- Lord, T., & Reichsman, F. (2018). A dashing new look into dragon genetics. @Concord, 22(2), 10-11.
- Dorsey, C., & Reichsman, F. (2015). Dragons fly higher with new projects. @Concord, 19(2), 14-15.
Geniventure is freely available! Find the game, student handouts, pre- and post-assessments, an online course for teachers, and other resources on the STEM Resource Finder.