 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 publisher model and the platform model [Oxman et al. (2014)]. In the publisher model, the system is designed with pre-existing content, often based on textbooks from the publisher. Pearson’s MyLabs (using Knewton [Jose (2016)] for its adaptive functionality), McGraw-Hill’s LearnSmart and ALEKS [Falmagne et al. (2006)] are established examples of this model. The publisher model has been very successful in K-12, where course content often have to comply with national standards. However, the overall adoption rate of adaptive learners that use the publisher model in higher education has been very low, and has been mostly restricted to research projects [Essa (2016)]. Limitations in tailoring the course content and the high per student cost of using these systems are among the main factors that contribute to the low adoption rate.
The platform model provides a content-agnostic system infrastructure that enables the teaching team to develop and author the content of their course. Smart Sparrow [Sparrow (2016)] and many learning management systems such as Desire2Learn, Loudcloud and edX that incorporate adaptive functionality into their course building tools follow this model. The platform model is relatively new and mostly suffers from an operational limitation rather than a technological one; implementing adaptivity in a course requires a large amount of new content and object tagging, which introduces a significant overhead for the teaching time. RiPPLE uses crowdsourcing to overcome this challenge, lowering the required workload associated with the adoption of adaptive learning using the platform model.
2.3 Recommender Systems in Technology Enhanced Learning
RecSysTEL is an active and rapidly evolving research field. For example, Drachsler2015 perform an extensive classification of 82 different RecSysTEL environments, and Erdt2015 review the various evaluation strategies that have been applied in the field. Together these articles provide recent comprehensive surveys that consider more than 200 articles spanning over 15 ye