Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because the existing general packages often lack of a convenient user interface, and are too complex or inadequate for these goals. Since ANN's algorithms, types and applications grow regularly, this solution becomes more and more complex and inefficient. In this paper, we present Visual NNet, a learning-oriented ANN's simulation environment, which overcomes this problem by reusing Matlab Neural Networks Toolbox (MNNT), a well-known, comprehensive and robust ANN implementation. Visual NNet combines an on-purpose learning oriented design with the advantages of an ANN's implementation like MNNT. Furthermore, reusing MNNT has done Visual NNet development more cost-effective, fast and reliable.
The paper aims to analyse several scientific approaches how to evaluate, implement or choose learning content and software suitable for personalised users/learners needs. Learning objects metadata customisation method as well as the Method of multiple criteria evaluation and optimisation of learning software represented by the experts' additive utility function are analysed in more detail. The value of the experts' additive utility function depends on the learning software quality evaluation criteria, their ratings and weights. The Method is based on the software engineering Principle which claims that one should evaluate the learning software using the two different groups of quality evaluation criteria - `internal quality' criteria defining the general software quality aspects, and `quality in use' criteria defining software personalisation possibilities. The application of the Method and Principle for the evaluation and optimisation of learning software is innovative in technology enhanced learning theory and practice. Application of the method of the experts' (decision makers') subjectivity minimisation analysed in the paper is also a new aspect in technology enhanced learning science. All aforementioned approaches propose an efficient practical instrumentality how to evaluate, design or choose learning content and software suitable for personalised learners needs.