Exploring General Morphological Analysis and Providing Personalized Recommendations to Stimulate Creativity with ReaderBench

Computer Supported Collaborative Learning (CSCL) has gained a steadily increasing role as it helps students to better comprehend through its synergistic effect, mediated by technology. In line with CSCL learning paradigm, our approach is centered on creativity stimulation which is facilitated by a deeper understanding of the dialog. This paper introduces new extended views for our ReaderBench framework, as well as a novel recommendations engine. Our General Morphological Analysis (GMA) implementation is based on the keywords extraction mechanism provided by ReaderBench, alongside with the similar concepts inferred using the lexicalized ontology WordNet, Latent Semantic Analysis (LSA), and Latent Dirichlet Analysis (LDA) semantic models. We also include a comprehensive case study to detail the new processing workflows that integrate voices (i.e., participants’ points of view), keywords identification, and text cohesion in order to recommend personalized learning resources.