Universities continue to rapidly evolve to address the needs of diverse, growing student populations, while embracing advances in pedagogy and technology. With the revolution in big data, universities are striving to utilise their rich and complex digital data on learners towards the personalisation of education.
At The University of Queensland, we have developed an adaptive, student-facing learning platform called RiPPLE (Recommendation in Personalised Peer Learning Environments) that provides personalised content and learning support at scale. RiPPLE employs exemplary techniques from the fields of machine learning, crowdsourcing, learning analytics and recommender to provide the following main functionalities:
Co-creation: RiPPLE empowers learners to contribute to co-creation of learning content.
Knowledge tracing: RiPPLE approximates learners’ knowledge states based on their interactions with the platform. It uses a variety of visualisations to enable learners to track their knowledge states and progress.
Content recommendation: RiPPLE uses the knowledge state of learners to recommend formative exercises tailored to the needs of each individual.
Peer Learning Support: RiPPLE recommends peer learning sessions based on the availability, learning preferences and needs of individuals. Students are given the opportunity to provide peer learning support, seek peer learning support or find study partners.
Gamification: RiPPLE uses leaderboards and a badging system to increase student motivation and performance.
The RiPPLE platform has been released as an open-source package111https://github.com/hkhosrav/RiPPLE-Core/ under the Apache 2.0 license and is freely available for usage in non-commercial settings. A prototype of the system is accessible through a GitHub account222https://hkhosrav.github.io/RiPPLE-Core/#/. RiPPLE can use the Learning Tools Interoperability (LTI) standard to integrated into many popular learning management systems including Blackboard, Moodle and Canvas. It is designed to be scalable, allowing the system to be easily adopted by many courses across different faculties in many universities. It is also designed to be sustainable, requiring very low maintenance from the teaching team.
The rest of this paper is organised as follows: Section 2 provides an overview of the related work on knowledge tracing, adaptive learning, recommender systems in technology enhanced learning (RecSysTEL), and reciprocal recommendation. Section 3 describes the data sources, functionality, the main aims and outputs of RiPPLE using a formal notation, which can be used by educational researchers. Section 4 presents an overview of the main features and functionalities that are supported by RiPPLE including creating and answering questions, knowledge tracing and recommending questions, reciprocal peer recommendation, leaderboards, personal profiles, and a page designed for instructors. Section 5 discusses the expected benefits students, instructors, and educational researchers will receive from using RiPPLE. Section 6 provides information on the implementation of the RiPPLE Client and Section 7 provides information on the implementation of the RiPPLE Server. Finally, Section 8 provides guidelines around contribution to RiPPLE.
2 Related Work
2.1 Knowledge Tracing
Knowledge tracing is the task of modelling the knowledge state of students so that their future performance on learning activities can accurately be predicted [Corbett and Anderson (1994)]. The problem of knowledge tracing was introduced, and has been heavily studied within the intelligent tutoring community [Corbett (2001)]. The Bayesian Knowledge tracing (BKT) algorithm [Corbett and Anderson (1994)]
is one of the most prominent methods used for knowledge tracing. BKT uses Hidden Markov Models to capture the student knowledge states as a set of binary variables representing whether or not a concept has been mastered.
BKT has recieved significant attention and improvement since it was first proposed. Baker2008 introduced slipping and guessing parameters; slipping refers to