International audienceMany modern web-based systems provide a ‘responsive' design that allows material and services to be accessed on mobile and desktop devices, with the aim of providing ubiquitous access. Besides offering access to learning materials such as podcasts and videos across multiple locations, mobile, wearable and ubiquitous technologies have some additional affordances that may enable new forms of learning on MOOCs. We can divide these into two categories: firstly, context-sensitive features including delivery of content for a specific location, enabling a seamless continuity of learning across settings, and linking people in a location with others in a virtual representation of that place; secondly social learning opportunities to connect people as they move within and across locations, to enable crowd-sourced learning. In this paper we explore these aspects of mobile and accessible learning for MOOCs, drawing on examples from MOOC courses, mobile toolkits, and crowd-sourced learning sites

Assistive robots introduce a new paradigm for developing advanced personalized services. At the same time, the variability and stochasticity of environments, hardware and unknown parameters of the interaction complicates their modelling, as in the case of staircase traversal. For this task, we propose to treat the problem of robot configuration control within a reinforcement learning framework, using policy gradient optimization. In particular, we examine the use of safety or traction measures as a means for endowing the learned policy with desired properties. Using the proposed framework, we present extensive qualitative and quantitative results where a simulated robot learns to negotiate staircases of variable size, while being subjected to different levels of sensing noise.

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This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this dropout. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider Open-Classrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners' engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.