er 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 years. Collaborative filtering (CF) identifies similar users and provides recommendations based upon their usage patterns. CF has been extensively employed in RecSysTEL; an early LAK paper, Verbert2011 evaluated and compared the performance of different CF techniques on educational data sets, showing that the best choice of algorithm is data dependent. In a more recent study, Kopeinik2017 also concluded that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset. In combining educational data sets with social networks, Cechinel2013 used CF to predict the utility of items for users based on their interest and the interest of the network of the users around them. Similarly, Fazeli2014 proposed a graph-based approach that uses graph-walking for improving performance on educational data sets.
One important way in which RecSysTEL has been used in an educational setting is to recommend personalized learning objects. Thus, Lemire2005 used inference rules to provide context aware recommendation on learning objects, and Mangina2008 recommend documents and resources within e-learning environments to expand or reinforce knowledge. Interestingly, Gomez-Albarran2009 combined content based filtering with collaborative filtering to make recommendations in a student authored repository. When recommending learning objects (e.g. questions) related to student, Cazella2010 provided a semi-automated, hybrid solution based on CF (nearest neighbor) and rule based filtering, while Thai-Nghe2011 used students’ performance predictions to recommend more appropriate exercises. CF techniques (basic, biased, and tensor matrix factorization) were used to address a number of different student behaviors and to model the temporal effect of students improving over time. Recently, Imran2016 provided an automated solution to personalize a learning management systems (LMS) using advanced learners’ profiles to encapsulate their expertise level, prior knowledge, and performance in the course. Th