To be successful in Higher Education, students must acquire good self-regulation and learning skills. Past studies have reported that undergraduate students are overconfident in recognizing their self-regulatory strategies. This overconfidence can be detrimental during the first years of their undergraduate program if they are not properly nurtured. The lack of students’ motivation, self-regulation and time management strategies can lead to higher rates of drop-out. In this sense, student-facing learning tools can provide timely feedback to support awareness, strengthen this self-regulation and time management skills, and thus be instrumental for students in attaining their learning goals. In this paper, we present the TMBQ-LT, a student-facing tool that consists of 1) a set of questions derived from the Time Management Behavior Questionnaire (TMBQ), 2) a visualization showing student’s time management (TM) predispositions and 3) tailored recommendations based on students’ self-reported TM skills. This paper illustrates a case study on the deployment of the TMBQ - LT by students from three HE institutions and provides recommendations for future implementations and adoption of the tool.

Invariant descriptors of point and rigid-body motion trajectories have been proposed in the past as representative task models for motion recognition and generalization. Currently, no invariant descriptor exists for representing force trajectories, which appear in contact tasks. This article introduces invariant descriptors for force trajectories by exploiting the duality between motion and force. Two types of invariant descriptors are presented depending on whether the trajectories consist of screw or vector coordinates. Methods and software are provided for robustly calculating the invariant descriptors from noisy measurements using optimal control. Using experimental human demonstrations of 3-D contour following and peg-on-hole alignment tasks, invariant descriptors are shown to result in task representations that do not depend on the calibration of reference frames or sensor locations. The tuning process for the optimal control problems is shown to be fast and intuitive. Similar to motions in free space, the proposed invariant descriptors for motion and force trajectories may prove useful for the recognition and generalization of constrained motions, such as during object manipulation in contact.

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.

Invariant representations of demonstrated motion trajectories provide context-independent motion models that can be used in motion recognition and generalization applications such as robot programming by demonstration. In practice, the use of invariant representations is still limited because their numerical calculation from a demonstrated trajectory is complicated by sensitivity to measurement noise and singularities, yielding inaccurate invariant functions that do not correspond well with the original trajectory. This paper improves the calculation of invariant representations for point and rigid-body motions by reformulating their calculation as an optimization problem that minimizes the error between the trajectory reconstructed from the invariant representation and the measured trajectory. Robustness against noise and singularities is ensured through the addition of regularization terms on the invariants. Simulations and real motion experiments show that the accuracy of the calculated invariant representations greatly improves with respect to standard smoothing methods. These results encourage future developments of motion recognition and generalization applications based on invariant trajectory representations.

Our work focuses on a multi-institutional implementation and evaluation of a Learning Analytics Dashboards (LAD) at scale, providing feedback to N=337 aspiring STEM (science, technology, engineering and mathematics) students participating in a region-wide positioning test before entering the study program. Study advisors were closely involved in the design and evaluation of the dashboard. The multi-institutional context of our case study requires careful consideration of external stakeholders and data ownership and portability issues, which gives shape to the technical design of the LAD. Our approach confirms students as active agents with data ownership, using an anonymous feedback code to access the LAD and to enable students to share their data with institutions at their discretion. Other distinguishing features of the LAD are the support for active content contribution by study advisors and LATEX type-setting of question item feedback to enhance visual recognizability. We present our lessons learnt from a first iteration in production.

This paper presents an evaluation of a learning dashboard that supports the dialogue between a student and a study advisor. The dashboard was designed, developed, and evaluated in collaboration with study advisers. To ensure scalability to other contexts, the dashboard uses data that is commonly available at any higher education institute. It visualizes the grades of the student, an overview of the progress through the year, his/her position in comparison with peers, sliders to plan the next years and a prediction of the length of the bachelor program for this student in years based on historic data. The dashboard was deployed at KU Leuven, Belgium and used in September 2017 to support 224 sessions between students and study advisers. We observed twenty of these conversations. We also collected feedback from 101 students with questionnaires. Results of our observations indicate that the dashboard primarily triggers insights at the beginning of a conversation. The number of insights and the level of these insights (factual, interpretative and reflective) depends on the context of the conversation. Most insights were triggered in conversations with students doubting to continue the program, indicating that our dashboard is useful to support difficult decision-making processes.

In programming by demonstration, generalization is necessary to apply demonstrated motions in novel situations. Many existing trajectory representations have poor generalization capabilities since they are built on trajectory coordinates that depend on the context in which the motion is recorded. In order to generalize, the user is typically required to perform a large number of varied demonstrations. This paper instead emphasizes the usefulness of an invariant trajectory representation to separate essential motion information from context-specific information of the recorded demonstrations. The invariants are interpreted as the control inputs of a dynamical system describing the evolution of the trajectory. New trajectories are generated for novel situations as the solution of a constrained optimal control problem in which context-specific information of the novel situation is encoded in the constraints. Results indicate how, starting from only a single demonstration, new trajectories can be generated in novel situations while maintaining similarity with the original demonstration. Invariance in trajectory representations therefore proves useful to reduce the number of necessary demonstrations to learn and apply new motions.

This paper presents an overview and comparison of minimal and complete rigid body motion trajectory descriptors, usable in applications like motion recognition and programming by demonstration. Motion trajectory descriptors are able to deal with potentially unwanted variations acting on the motion trajectory such as changes in the execution time, the motion's starting position, or the viewpoint from which the motion is observed. A suitable rigid body motion trajectory descriptor retains only the trajectory information relevant to the application. This paper compares different trajectory descriptors for rigid body motion and validates their usefulness for dealing with motion variation in a motion recognition experiment. Furthermore, a new type of invariant trajectory descriptor is introduced based on the Frenet-Serret formulas.

We propose a relational model for online object tracking during human activities using the Distributional Clauses Particle Filter framework, which allows to encode commonsense world knowledge such as qualitative physical laws, object properties as well as relations between them. We tested the framework during a packaging activity where many objects are invisible for longer periods of time. In addition, we extended the framework to learn the parameters online and tested it in a tracking scenario involving objects connected by strings.

This paper presents a scene graph for geometric relations between rigid bodies that keeps track of poses and twists of rigid bodies in the scene. The scene graph relies on semantic pose and twist representation, making it invariant to the actual coordinate representation at hand. This makes the scene graph more general and interoperable than most scene graphs currently available. The presented scene graph takes into account constraints imposed by particular coordinate representations, allows for constant poses, answers semantic pose and twist queries, and provides built-in semantic consistency checks. Since the scene graph also keeps track of the twist, it allows native twist calculations, as opposed to deriving the velocities from the poses in the graph. This paper comes with software released under a dual BSD/LGPLv2.1 license.

This paper presents the application of a Bayesian nonparametric time-series model to process monitoring and fault classification for industrial robotic tasks. By means of an alignment task performed with a real robot, we show how the proposed approach allows to learn a set of sensor signature models encoding the spatial and temporal correlations among wrench measurements recorded during a number of successful task executions. Using these models, it is possible to detect continuously and on-line deviations from the expected sensor readings. Separate models are learned for a set of possible error scenarios involving a human modifying the workspace configuration. These non-nominal task executions are correctly detected and classified with an on-line algorithm, which opens the possibility for the development of error-specific recovery strategies. Our work is complementary to previous approaches in robotics, where process monitors based on probabilistic models, but limited to contact events, were developed for control purposes. Instead, in this paper we focus on capturing dynamic models of sensor signatures throughout the whole task, therefore allowing continuous monitoring and extending the system ability to interpret and react to errors.

We introduce a probabilistic language and a fast inference algorithm for state estimation in hybrid dynamic relational domains with an unknown number of objects. More specifically, we apply Particle Filters to distributional clauses. The particles represent (partial) interpretations of possible worlds (with discrete and/or continuous variables) and the filter recursively updates its beliefs about the current state. We use backward reasoning to determine which facts should be included in the partial interpretations. Experiments show that our framework can outperform the classical particle filter and is promising for robotics applications.

Task specification models define the activities to be executed by a robot in order to achieve its goal. Classical examples are the sequences involved in assembly or pick and place tasks. This work introduces the preview coordination execution model, an extension to the traditional way in which the execution of such task specifications is coordinated at runtime. Instead of taking activities one-by-one as defined in the task specification model, preview coordination optimizes the task scheduling based on knowledge about the likelihood that not just the activities required by the current state can be executed, but that also one or more of those related to future states of the system can be activated. An experiment with mobile manipulation tasks illustrates the benefits of preview coordination.

Current state-of-the-art robot program development needs expert programmers. Moreover, most robot programs developed today are robot hardware and software specific, and therefore little reusable without modifications. This paper realizes easier robot (re-)programming, by software framework independent models that can be executed using different hard- and software platforms. First, the paper focuses on the formalization of the tasks to be fulfilled by a robot, more specifically constraint-based programming tasks using a Domain Specific Language (DSL). Second, it gives a reference implementation in Lua [1]. The presented DSL makes it easy to develop applications, yet is powerful to execute. It enables automatic model verification and code generation for different hard- and software platforms, diminishing code debugging efforts. Experimental validation shows the ease of creating an application and adapting it, the reduction of the amount of hand-written code, and the debugging aid offered through meaningful errors returned by model verification.

In this paper, the use of a coordinate-free representation for recognizing six DOF rigid body motion trajectories is experimentally validated. In the recognition part of this approach, the three-dimensional measured position trajectories of arbitrary and uncalibrated points attached to the rigid body are transformed to an invariant, coordinate-free representation of the rigid body motion trajectory. This representation is theoretically independent of the reference frame in which the motion is observed, the chosen marker positions, the linear scale (magnitude) of the motion, the time scale, and the motion profile. During the experiments, a person manipulated an object. The camera viewpoints, time scales, motions profiles, and linear or angular scales were changed between different motion recordings. The experimental results validate that not only in similar but also in different recording conditions, through using the invariant representation, the dependency on the parameters mentioned above are eliminated, and therefore better recognition results are obtained.

This paper presents an approach to recognize 6 DOF rigid body motion trajectories (3D translation + rotation), such as the 6 DOF motion trajectory of an object manipulated by a human. As a first step in the recognition process, 3D measured position trajectories of arbitrary and uncalibrated points attached to the rigid body are transformed to an invariant, coordinate-free representation of the rigid body motion trajectory. This invariant representation is independent of the reference frame in which the motion is observed, the chosen marker positions, the linear scale (magnitude) of the motion, the time scale and the velocity profile along the trajectory. Two classification algorithms which use the invariant representation as input are developed and tested experimentally: one approach based on a Dynamic TimeWarping algorithm, and one based on Hidden Markov Models. Both approaches yield high recognition rates (up to 95 % and 91 %, respectively). The advantage of the invariant approach is that motion trajectories observed in different contexts (with different reference frames, marker positions, time scales, linear scales, velocity profiles) can be compared and averaged, which allows us to build models from multiple demonstrations observed in different contexts, and use these models to recognize similar motion trajectories in still different contexts.

This paper discusses the theoretical background and practical implementation of a large-scale, low-performance haptic remote control setup. The experimental system consists of a pair of KUKA Light Weight Robots (LWR) coupled to a Willow Garage Personal Robot (PR2) via two different robotic frameworks. The haptic “performance” is, of course, not comparable to dedicated haptic applications, but has its use as a test-bed for interaction between “legacy” service robot systems, that have not been especially designed for mutual haptic interaction. We discuss some major application problems, and the future work needed for nonuniform robot coupling. Beside haptic coupling, we provide the human operator with visual feedback. To this end, the head movements of the human operator are coupled to the head movement of the PR2 and the images of the eye cameras are displayed to the human operator using a wearable display. The presented teleoperation application is furthermore an example of the integration of two component-based robotic frameworks namely OROCOS (Open Robot Control Software)and ROS (Robot Operating System) Experimental results regarding the haptic coupling are presented using an “artistic” painting task for qualitative results, and a hard contact at the slave side for quantitative results.

This paper proposes a novel online two-level multitarget tracking and detection (MTTD) algorithm. The algorithm focuses on multitarget detection and tracking for the case of multiple measurements per target and for an unknown and varying number of targets. Information is continuously exchanged in both directions between the two levels. Using the high level target position and shape information, the low level clusters the measurements. Furthermore, the low level features automatic relevance detection (ARD), as it automatically determines the optimal number of clusters from the measurements taking into account the expected target shapes. The high level's data association allows for a varying number of targets. A joint probabilistic data association algorithm looks for associations between clusters of measurements and targets. These associations are used to update the target trackers and the target shapes with the individual measurements. No information is lost in the two-level approach since the measurement information is not summarized into features. The target trackers are based on an underlying motion model, while the high level is supplemented with a filter estimating the number of targets. The algorithm is verified using both simulations and experiments using two sensor modalities, video and laser scanner, for detection and tracking of people and ants.

This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. The modeling rigorously explains all model assumptions and parameters, improving the physical interpretation of all parameters and the intuition behind the model choices. With respect to the state of the art model [1], this paper proposes: (i) a different functional form for the probability of range measurements caused by unexpected objects, (ii) an intuitive explanation for the discontinuity encountered in the cited paper, and (iii) a reduction in the number of model parameters, while maintaining the same representational power for experimentally obtained data. The proposed beam model is called RBBM, short for rigorously Bayesian beam model. A maximum-likelihood estimation and a variational Bayesian estimation algorithm (both based on expectation-maximization) are proposed to learn the model parameters.

This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and all model parameters have a physical interpretation. This approach results in a transparent and intuitive model. With respect to the state of the art beam model this paper: (i) proposes a different functional form for the probability of range measurements caused by unmodeled objects, (ii) intuitively explains the discontinuity encountered in te state of the art beam model, and (iii) reduces the number of model parameters, while maintaining the same representational power for experimental data. The proposed beam model is called RBBM, short for Rigorously Bayesian Beam Model. A maximum likelihood and a variational Bayesian estimator (both based on expectation-maximization) are proposed to learn the model parameters. Furthermore, the RBBM is extended to a full scan model in two steps: first, to a full scan model for static environments and next, to a full scan model for general, dynamic environments. The full scan model accounts for the dependency between beams and adapts to the local sample density when using a particle filter. In contrast to Gaussian-based state of the art models, the proposed full scan model uses a sample-based approximation. This sample-based approximation enables handling dynamic environments and capturing multi-modality, which occurs even in simple static environments.

This paper shows the application of a systematic approach for constraint-based task specification for sensorbased robot systems [1] to a laser tracing example. This approach integrates both task specification and estimation of geometric uncertainty in a unified framework. The framework consists of an application independent control and estimation scheme. An automatic derivation of controller and estimator equations is achieved, based on a geometric task model that is obtained using a systematic task modeling procedure. The paper details the systematic modeling procedure for the laser tracing task and elaborates on the task specific choice of two types of task coordinates: feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and uncertainty coordinates to model geometric uncertainty. Furthermore, the control and estimation scheme for this specific task is studied. Simulation and real world experimental results are presented for the laser tracing example.

This paper presents a unified task specification formalism and a unified control scheme for the lowest control level of sensor-based robot tasks. The formalism is based on: (i) the integration of any sensor that provides (direct or indirect) distance (and time derivatives) and force information; (ii) the possibility to use multiple "Tool Centre Points", e.g. defined relative to the robot end effector, other links or the environment; (iii) the integration of optimization functions for underconstrained as well as overconstrained specifications with linear constraints; (iv) the integration of on-line estimators; and (v) compatibility with all major low level control approaches. The unified formalism applies to the whole range from industrial manipulators over cooperating robots to humanoid robots, and from pure position control tasks over industrial processes to interaction between a humanoid robot and its environment.