Awarding gaps have been commonly observed between different socio-demographic categories of students, especially in the domains of sociology and learning science. Recent research in the context of OUAnalyse has shown that using Learning Analytics models could be exploited to reduce these gaps, and therefore contribute to making the learning process more inclusive and equitable. Following this line of research, we are currently focusing on the potential of cognitive effort models and activity recommenders built upon these cognitive effort models to enhance inclusiveness.

We developed a new web-based learning prototype called CERSEI (Cognitive Effort based Recommender System for Enhancing Inclusiveness). From a research point of view, CERSEI allows us to collect behavioural data and subjective effort ratings that can be used to train our effort model. Our goal is to test the hypothesis that the effort is different for different categories of students (as indicated previously in the literature), to have a better understanding of how different categories of students exert effort while learning, and to have a better understanding of the extent to which recommendations of learning activities can be useful depending on the categories of students.

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