RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities
This provides a practical tool for educators to enhance student learning through adaptive recommendations, though it appears incremental by integrating existing research areas.
The paper tackles the problem of personalized learning by developing RiPPLE, a platform that recommends crowdsourced learning activities based on students' knowledge states, with initial results showing measurable learning gains and positive student perceptions in a pilot with 453 students.
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.