Abstract
This paper proposes a structure driven approach to assess graph-based exercises. Given two graphs, a solution and an attempt of a student, this approach computes a mapping between the node sets of both graphs that maximizes the student's grade, as well as a description of the differences between the two graph. The proposed algorithm uses heuristics to test the most promising mappings first and prune the remaining when it is sure that a better mapping cannot be computed. The proposed algorithm is applicable to any type of document that can be parsed into its graph-inspired data model. This data model is able to accommodate diagram languages, such as UML or ER diagrams, for which this kind of assessment is typically used. However, the motivation for developing this algorithm is to combine it with other assessment models, such as the test case model used for programming language assessment. The proposed algorithm was validated with thousands of graphs with different features produced by a synthetic data generator. Several experiments were designed to analyse the impact of different features such as graph size, and amount of difference between solution and attempt.

Abstract

Abstract

Abstract
Existing gami?cation services have features that preclude their use by e-learning tools. Odin is a gami?cation service that mimics the API of state-of-the-art services without these limitations. This paper describes Odin, its role in an e-learning system architecture requiring gami?cation, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) speci?cation.

Abstract

Abstract
We address the problem of providing users, namely non specialists, with out-of-the-box, programmable, Wireless Sensor-Actuator Networks (WSN). The idea is that users get a package containing a gateway and an undetermined number of nodes, pre-configured to work as a self-organized wireless mesh. Each node comes with two pre-installed components: a small operating system and a virtual machine. The user can then use a simple, domain-specific, programming language to implement periodic tasks that are compiled into byte-code, and can be sent to the nodes for execution. At the nodes, the operating system manages a task table and schedules non-preemptive tasks for execution using the virtual machine. No subtle hardware or software configuration is required from the user as these details are abstracted away by the virtual machine. We developed a full specification for a data-layer that follows the aforementioned guidelines and implemented a complete prototype, integrated in our own Publish/Subscribe middleware called SONAR. In this paper we report the first results of using the prototype as compared to using the low level programming tools provided with the hardware. We measure a small increase in both resource consumption and processing overhead suggesting that this data-layer can be used effectively in WSN, even in cases where nodes have very limited hardware resources.