e approach used association rule mining to create the learning object recommendations.
Matrix factorization (MF) is one of the most established techniques used in CF; however, despite its success in RecSys, MF has rarely been used in RecSysTEL. Out of the 124 papers referenced by Drachsler2015, only two papers directly use it [Salehi (2013), Thai-Nghe et al. (2011)]. This is somewhat surprising; MF has been put to good use in EDM for generating latent profiles of student expertise and so ought to combine with RecSysTEL in a straightforward manner. Indeed, the intelligent tutoring systems that appear to be utilized in that body of work could be seen as closely related to RecSysTEL, although they tend to give students less autonomy to accept or reject the pathways chosen for them [Chen (2008)]. MF is particularly powerful in modeling students’ performance and knowledge, because it implicitly incorporates guess and slip factors as latent factors [Thai-Nghe et al. (2011)]. The current implementation of RiPPLE uses the recommender system of khosravi2017, which employs MF.
2.4 Reciprocal Recommendation
Reciprocal recommender systems has a literature rich with applications in online dating [Pizzato et al. (2013)], job matching [Hong et al. (2013)], and social networking [Guy (2015)], each highly tailored by the best consideration of the specific needs and constraints of the immediate environment. Fundamentally they entail operationalised user preferences and seek solutions such as a list of recommendations that match those preferences to a higher degree than competing items. In reciprocal recommendation, items are usually other users whose preferences must also be fulfilled and so entail a higher level of complexity than other recommender systems [Pizzato et al. (2010)]. Much of the research in social recommending has been developed and evaluated in existing social networks and particularly online dating sites where the site and/or the individual users may have a substantial data history which can be used to train machine learning algorithms [Cai et al. (2011), Chen and Nayak (2013), Kutty et al. (2014)]. In peer recommending the environment may be a newly formed cohort of learners (such as a first-year university course) who have no data history but can volunteer preferences on a narrow selection of relevant variables for the purposes of matching.
By their nature, recommender systems across domains will exhibit similarities to a high degree. All systems incontrovertibly share the same fundamental goal - to provide recommendations that are well-received by users according to their preferences, explicit and implicit, in an otherwise overwhelming information environment where the likelihood of users successfully finding preferred items without technological assistance is very low. However the nature of domain-specific information and definition of a successful recommendation is so heavily context- and goal-dependent that little more than the general way of thinking can be adapted or generalised from existing systems to new domains. This is particularly true of the formulation of user preference models upon which recommendations are to be based, making them necessarily bespoke.
As such, while reciprocal peer recommendation has similarities to traditional recommendations in education and reciprocal recommendations in other domains, it does have a distinct nature. Although some primary research has been done on utilising peer learning and support for improving learning and enhancing the learning experience of students, the area remains fertile for many research and development opportunities. In this paper we introduce a platform that can enable adoption of peer support systems for both large on-campus and online courses with competency-based user preference models.
The current implementation of RiPPLE uses the reciprocal peer recommendation algorithm of [Potts et al. (2018)].
3 Formal Notation
This section describes the data sources, functionality, the main aims and outputs of RiPPLE using a formal notation.
Users and Questions:
Let