Simulating Light-Weight Personalized Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies

Recommender systems for e-learning demand specific pedagogy-oriented & hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalized recommender systems (PRS), that offer the learners customized advise on which learning actions or programs to study next. Such systems should also be practically feasible & be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis & optimization of PRS requirements prior to starting the costly process of their development, & practical implementation (including testing & revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, & faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, & faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating). Adapted from the source document.