RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities

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 themse...

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Published in:Journal of Learning Analytics Vol. 6; no. 3; pp. 91 - 105
Main Authors: Khosravi, Hassan, Kitto, Kirsty, Williams, Joseph Jay
Format: Journal Article
Language:English
Published: Society for Learning Analytics Research 01.01.2019
Subjects:
Computer Software
Data Analysis
Foreign Countries
Introductory Courses
Learning Activities
Management Systems
Peer Teaching
Teaching Methods
Undergraduate Students
Australia
ISSN:1929-7750, 1929-7750
Online Access:Get full text
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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.
AbstractList 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.
Audience Higher Education
Postsecondary Education
Author Khosravi, Hassan
Williams, Joseph Jay
Kitto, Kirsty
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SubjectTerms Computer Software
Data Analysis
Foreign Countries
Introductory Courses
Learning Activities
Management Systems
Peer Teaching
Teaching Methods
Undergraduate Students
Title RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities
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