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This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Master's course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the feedback is effective in addressing the level of knowledge of the individual and guiding him/her toward a greater understanding of particular concepts. In contrast, there is no evidence that learning required in programming problems, where students develop higher-level thinking according to Bloom's taxonomy, was exercised by using MCQs. [ABSTRACT FROM AUTHOR]

Abstract: Research towards an innovative solution to the problem of automated updating of road network databases is presented. It moves away from existing methods where vendors of road network databases either go through the time consuming and logistically challenging process of driving along roads to register changes or use update methods that rely on remote sensing images. The solution presented here would allow users of road network dependent applications (e.g. in-car navigation system or Sat Nav) to passively collect characteristics of any “unknown route” (departure from the known roads in the database) on behalf of the provider. These data would be processed either by an on-board artificial neural network (ANN) or transferred back to the Sat Nav provider and input into their ANN along with similar track data provided by other service users, to decide whether or not to automatically update (add) the “unknown road” to the road database. The solution presented here addresses the feasibility of identifying roads and assigning them into classes from recorded global positioning system (GPS) trajectory data. GPS trajectory data collected in London are analysed using a snap-drift neural network (SDNN) which categorises them into their strongest natural groupings, by combining clustering with feature detection in a single ANN. The key variables required are discussed. We have demonstrated that the SDNN offers a fast method of learning that preserves feature discovery. Using only GPS trajectory information, the SDNN is able to group collected points to reveal travelled road segments. The results showed that relying only on the winning node, a grouping accuracy of about 71% is achieved compared to 51% from learning vector quantisation (LVQ). On analysis and further experimentation with the SDNN d-nodes a grouping accuracy of nearly 100% was achieved, but with a high count of unique d-node combinations. [Copyright &y& Elsevier]

We present a neural network for real-time learning and mapping of patterns using an external performance indicator. In a non-stationary environment where new patterns are introduced over time, the learning process utilises a novel snap-drift algorithm that performs fast, convergent, minimalist learning (snap) when the overall network performance is poor and slower, more cautious learning (drift) when the performance is good. Snap is based on a modified form of Adaptive Resonance Theory (CGIP 37(1987)54); and drift is based on Learning Vector Quantization (LVQ) (Proc. IJCNN 1(1990a)545). The two are combined within a semi-supervised learning system that shifts its learning style whenever it receives a significant change in performance feedback. The learning is capable of rapid re-learning and re-stabilisation, according to changes in external feedback or input patterns. We have incorporated this algorithm into the design of a modular neural network system, Performance-guided Adaptive Resonance Theory (P-ART) (Proc. IJCNN 2(2003)1412; Soft computing systems: Design, Management and application, IOS Press, Netherland, 2002; pp. 21–31). Simulation results show that the system discovers alternative solutions in response to significant changes in the input patterns and/or in the environment, which may require similar patterns to be treated differently over time. The simulations involve attempting to optimise the selection of network services in a non-stationary, real-time active computer network environment, in which the factors influencing the required selections are subject to change. [Copyright &y& Elsevier]