RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities

Authors

DOI:

https://doi.org/10.18608/jla.2019.63.12

Keywords:

Adaptive learning, Crowdsourcing, Recommender Systems

Abstract

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 RiPPLE platform led to measurable learning gains and that students perceived the platform as beneficially supporting their learning.

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Published

2019-12-13

How to Cite

Khosravi, H., Kitto, K., & Williams, J. J. (2019). RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities. Journal of Learning Analytics, 6(3), 91–105. https://doi.org/10.18608/jla.2019.63.12