A game-based approach to the teaching of object-oriented programming languages

Students often have difficulties when trying to understand the concepts of object-oriented programming (OOP). This paper presents a contribution to the teaching of OOP languages through a game-oriented approach based on the interaction with tangible user interfaces (TUIs). The use of a specific type of commercial distributed TUI (Sifteo cubes), in which several small physical devices have sensing, wireless communication and user-directed output capabilities, is applied to the teaching of the C# programming language, since the operation of these devices can be controlled by user programs written in C#. For our experiment, we selected a sample of students with a sufficient knowledge about procedural programming, which was divided into two groups: The first one had a standard introductory C# course, whereas the second one had an experimental C# course that included, in addition to the contents of the previous one, two demonstration programs that illustrated some OOP basic concepts using the TUI features. Finally, both groups completed two tests: a multiple-choice exam for evaluating the acquisition of basic OOP concepts and a C# programming exercise. The analysis of the results from the tests indicates that the group of students that attended the course including the TUI demos showed a higher interest level (i.e. they felt more motivated) during the course exposition than the one that attended the standard introductory C# course. Furthermore, the students from the experimental group achieved an overall better mark. Therefore, we can conclude that the technological contribution of Sifteo cubes – used as a distributed TUI by which OOP basic concepts are represented in a tangible and a visible way – to the teaching of the C# language has a positive influence on the learning of this language and such basic concepts

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA