Sponsored Research: ALICE
This is a project of the Biomathematics Research Group
| Funding Agency: | National Science Foundation Award #1645325 |
| Research Team: | Juan B. Gutierrez (PI), University of Georgia Jonathan Arnold (Co-PI), University of Georgia Pedro Portes (Co-PI), University of Georgia Co-Investigators from the BRG Karen Aguar, Mehdi Asefi, Saeid Safaei |
| Funding: | $300K |
| Project Period: | 2016-2019 |
Available at http://mathresearch.utsa.edu/alice
We have created ALICE (Adaptive Learning for Interdisciplinary Learning Environments), a Web-based information system intended to contribute to interdisciplinary education related to mathematical biology.
The realization of the transdisciplinary paradigm that would allow any scientist to cross scales, clashes with the reality of interdisciplinary training. From K-16 science classrooms, to graduate studies and professional teams, STEM education has traditionally consisted of a hierarchical and deterministic organization of concepts, i.e. tree-like structures with starting points and well defined paths. The preference of a fixated learning pathway to navigate through scientific material ignores each student’s prior learning and abilities. Tradition in science education also assumes a clear separation between various scientific disciplines, thus neglecting problem-solving structures of multi-disciplinary and trans-disciplinary scientific environments. ALICE is a Web-based information system that can be used at multiple educational levels. Instead of forcing all students to go through the same curriculum at the same pace, ALICE connects a series of atomic units of knowledge (termed lexias) though a dynamic path and presents it to the student for the purpose of acquiring a set of competencies. The metaphor of the tree in traditional STEM education is replaced in ALICE by a dense rhizome-like network that does not privilege a particular path, but instead offers a milieu for traversal. In practice, it is the student during the learning process who makes an abstract knowledge network come to a unique realization. ALICE generates individualized development plans, according to previous experiences and current challenges. Furthermore, ALICE is designed to connect lexias from multiple subject matters, thus bypassing disciplinary barriers that in many cases are artificial. The principles behind ALICE are generalizable, and hence it has the potential to be used in K-16, graduate, and continuing education.