User Assessment in Serious Games and Technology-Enhanced Learning

1 Department of Naval, Electric, Electronic and Telecommunications Engineering, University of Genoa, Via all'Opera Pia 11/a, 16145 Genoa, Italy 2 Faculty of Business and Information Technology, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, Canada L1H 7K4 3Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA 4 Faculty of Computer Science, Universidad Complutense de Madrid, Ciudad Universitaria Universidad Complutense de Madrid, 28040 Madrid, Spai

Assessment in and of serious games: an overview

There is a consensus that serious games have a significant potential as a tool for instruction. However, their effectiveness in terms of learning outcomes is still understudied mainly due to the complexity involved in assessing intangible measures. A systematic approach—based on established principles and guidelines—is necessary to enhance the design of serious games, and many studies lack a rigorous assessment. An important aspect in the evaluation of serious games, like other educational tools, is user performance assessment. This is an important area of exploration because serious games are intended to evaluate the learning progress as well as the outcomes. This also emphasizes the importance of providing appropriate feedback to the player. Moreover, performance assessment enables adaptivity and personalization to meet individual needs in various aspects, such as learning styles, information provision rates, feedback, and so forth. This paper first reviews related literature regarding the educational effectiveness of serious games. It then discusses how to assess the learning impact of serious games and methods for competence and skill assessment. Finally, it suggests two major directions for future research: characterization of the player's activity and better integration of assessment in games

Robots that duplicate themselves: Theoretical principles and physical demonstrations

This dissertation primarily focuses on robotic self-replication including a theoretical framework and quantitative measures that can be applied to self-replicating systems. A new descriptive model for physical replicating systems is introduced based on three sets of components: an initial functional system, a set of resources, and a set of external elements. Robotic self-replication is viewed as a process by which an initial functional robot duplicates itself given a set of resource modules in a bounded environment. In order to assess physical self-replicating systems with respect to their structural properties and performance, two quantitative measures are defined. The first is the degree of self-replication which is a combined measure of structural complexity distribution across the modules and the ratio of the robot's complexity to the complexity of the modules. This quantifies an intuitive notion of many simple parts versus a few complex parts. The second is configurational entropy changes resulting from the self-replication process. Configurational entropy is used to measure the amount of uncertainty in the locations of modules. Entropy is also applied for articulated chains by using Fixman's method to compute the mass-metric tensor determinant (MMTD). This dissertation also presents a further extension of Fixman's method to compute the inverse of the generalized mass matrix for serial manipulators and polymer chains. Building on previous experimental work, two new self-replicating robots are constructed and presented in this dissertation. The first prototype contains an initial functional robot, a set of modules to form a replica, and a structured environment including tracks, barcodes, contact codes, etc. This system duplicates itself in a similar fashion to the previous prototypes developed in the Robot and Protein Kinematics Laboratory at Johns Hopkins University. However, it shows a progression toward a robot consisting of an increased number of modules while performing more complex tasks. The second prototype duplicates itself through mitosis. This system has several unique properties including its mechanical design and self-replication strategy that distinguish it from any other existing systems

Integrated system architecture with mixed-reality user interface for virtual-physical hybrid swarm simulations

Abstract This paper introduces a hybrid robotic swarm system architecture that combines virtual and physical components and enables human–swarm interaction through mixed reality (MR) devices. The system comprises three main modules: (1) the virtual module, which simulates robotic agents, (2) the physical module, consisting of real robotic agents, and (3) the user interface (UI) module. To facilitate communication between the modules, the UI module connects with the virtual module using Photon Network and with the physical module through the Robot Operating System (ROS) bridge. Additionally, the virtual and physical modules communicate via the ROS bridge. The virtual and physical agents form a hybrid swarm by integrating these three modules. The human–swarm interface based on MR technology enables one or multiple human users to interact with the swarm in various ways. Users can create and assign tasks, monitor real-time swarm status and activities, or control and interact with specific robotic agents. To validate the system-level integration and embedded swarm functions, two experimental demonstrations were conducted: (a) two users playing planner and observer roles, assigning five tasks for the swarm to allocate the tasks autonomously and execute them, and (b) a single user interacting with the hybrid swarm consisting of two physical agents and 170 virtual agents by creating and assigning a task list and then controlling one of the physical robots to complete a target identification mission