  This paper presents a platform called RiPPLE (Recommendation in Personalised
Peer-Learning Environments) that recommends personalized learning activities to
students based on their knowledge state from a pool of crowdsourced learning
activities that are generated by educators and the students themselves. RiPPLE
integrates insights from crowdsourcing, learning sciences, and adaptive
learning, aiming to narrow the gap between these large bodies of research while
providing a practical platform-based implementation that instructors can easily
use in their courses. This paper provides a design overview of RiPPLE, which
can be employed as a standalone tool or embedded into any learning management
system (LMS) or online platform that supports the Learning Tools
Interoperability (LTI) standard. The platform has been evaluated based on a
pilot in an introductory course with 453 students at The University of
Queensland. Initial results suggest that the use of the \name platform led to
measurable learning gains and that students perceived the platform as
beneficially supporting their learning.
