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 the situation where a student has the required skill for answering a question but mistakenly provides the wrong answer; and guessing refers to the situation where a student provides the right answer despite not having the required skill for solving the problem. Later on, pardos2011kt effectively extended BKT to capture item difficulty, which led to improve prediction accuracy. More recently, yudelson2013individualized further improved BKT by introducing a new set of parameters capturing prior knowledge of individual learners.
Other algorithms with comparable or superior predictive power to BKT have also been proposed for knowledge tracing. cen2006learning introduced the Learning Factors Analysis Framework, and pavlik2009performance introduced the Performance Factor Analysis framework. khajah2014integrating incorporated Item Response theory (IRT) into knowledge tracing, and more recently, Piech2015 and sha2017neural used recurrent neural networks for deep knowledge tracing.
The current implementation of RiPPLE uses the knowledge tracing algorithm of khosravi2017
2.2 Adaptive Learning
Adaptive learning platforms use knowledge tracing to dynamically adjust the level or type of instruction based on individual student abilities or preferences [Paramythis and Loidl-Reisinger (2003)]. Use of adaptive platforms help personalise learning, and has been shown to improve or accelerate student performance [Yilmaz (2017)]. At a high level of generality all adaptive learning platforms rely on four interacting models: (1) a knowledge space modelling what the learners need to know, (2) a set of knowledge states modelling what students currently know, (3) a repository of learning objects, mapped to the concepts of the knowledge space, modelling the learning activities that are available to the students, (4) a recommender system modelling the extend to which different learning activities meet the learning needs of each of the students [Essa (2016)].
There are two main types of adaptive learning platforms referred to as the publish