This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity- based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.
Learning analytics dashboards are at the core of the LAK vision to involve the human into the decision-making process. The key focus of these dashboards is to support better human sense-making and decision-making by visualising data about learners to a variety of stakeholders. Early research on learning analytics dashboards focused on the use of visualisation and prediction techniques and demonstrates the rich potential of dashboards in a variety of learning settings. Present research increasingly uses participatory design methods to tailor dashboards to the needs of stakeholders, employs multimodal data acquisition techniques, and starts to research theoretical underpinnings of dashboards. In this paper, we present these past and present research efforts as well as the results of the VISLA19 workshop on "Visual approaches to Learning Analytics" that was held at LAK19 with experts in the domain to identify and articulate common practices and challenges for the domain. Based on an analysis of the results, we present a research agenda to help shape the future of learning analytics dashboards.
This paper presents LISSA (Learning dashboard for Insights and Support during Study Advice), a learning analytics dashboard designed, developed, and evaluated in collaboration with study advisers. The overall objective is to facilitate communication between study advisers and students by visualizing grade data that is commonly available in any institution. More specifically, the dashboard attempts to support the dialogue between adviser and student through an overview of study progress, peer comparison, and by triggering insights based on facts as a starting point for discussion and argumentation. We report on the iterative design process and evaluation results of a deployment in 97 advising sessions. We have found that the dashboard supports the current adviser-student dialogue, helps them motivate students, triggers conversation, and provides tools to add personalization, depth, and nuance to the advising session. It provides insights at a factual, interpretative, and reflective level and allows both adviser and student to take an active role during the session.