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Welcome to the ALOSI wiki!
Adaptive learning aims to provide more personalized experiences that lead to higher levels of engagement and learning gains. We see a need for more evidence and transparency on the factors that make adaptive learning effective, and systems to facilitate adaptive learning implementation and research.
The ALOSI framework facilitates adaptive learning implementation and research, and is composed of four types of components:
| Component | Role | Example platform |
|---|---|---|
| LMS | Learning Management System | edX, Canvas, or any LTI consumer |
| Content Source | Content hosting | Open edX or any LTI provider |
| Engine | Provides recommendations | ALOSI Engine |
| Bridge | Integration with LMS, data coordination, collection authoring | Bridge for Adaptivity |
The Bridge for Adaptivity facilitates interfacing with multiple types of external components:
- Multiple engines: comparison between more types of algorithmic strategies and systems.
- Multiple LMS: use in multiple types of learning management systems
- Multiple content sources: Use of heterogeneous types of content to construct adaptive sequences
See the navigation sidebar on the top right-hand side of this page for documentation pages, including setup steps for developers and user guides for instructors/researchers.
- ALOSI Adaptive Engine - https://github.com/harvard-vpal/adaptive-engine
- ALOSI library - https://github.com/harvard-vpal/alosi
- Ranked recommendation UI - https://github.com/harvard-vpal/alosi-recommend