Intelligent mooc for the disaster resilience dprof programme

The CADRE Project offers Intelligent MOOC for the disaster resilience DPROF programme (MOOC-DPROF). MOOC-DPROF aims at unlimited participation and open access via the Virtual Environment for the Built Environment Research to reduce knowledge shortfalls across the EU. PhD students registered in MOOC-DPROF differ by their knowledge levels, preferences, interests, goals, cognitive styles and learning styles. The basis of MOOC-DPROF is individual learning. The design of MOOC-DPROF is for it to run within the Moodle platform. PhD students are offered personalised learning materials in the form of digital textbooks, videos, audios as well as calculators, software, computer learning systems, an intelligent testing system, affective intelligent tutoring system, etc. A personalised MOOC-DPROF adapts the studies to individual needs. Upon completing the analysis of globally developed resilience management MOOCs, it was noticed that there is still no MOOC developed by applying biometric and intelligent systems in an integrated manner, something that has already been implemented with the MOOC-DPROF. The subsystems and a Case Study are briefly analysed in this paper

Using data-driven model-brain mappings to constrain formal models of cognition

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings