A variant of nearest-neighbor (NN) pattern classification and supervised learning by learning vector quantization (LVQ) is described. The decision surface mapping method (DSM) is a fast supervised learning algorithm and is a member of the LVQ family of algorithms. A relatively small number of prototypes are selected from a training set of correctly classified samples. The training set is then used to adapt these prototypes to map the decision surface separating the classes. This algorithm is compared with NN pattern classification, learning vector quantization, and a two-layer perceptron trained by error backpropagation. When the class boundaries are sharply defined (i.e., no classification error in the training set), the DSM algorithm outperforms these methods with respect to error rates, learning rates, and the number of prototypes required to describe class boundaries.

<p class="abstract"><span lang="EN-US">In recent years, universities have been under increased pressure to adopt e-learning practices for teaching and learning. In particular, the emphasis has been on learning management systems (LMSs) and associated collaboration tools to provide opportunities for sharing knowledge, building a community of learners, and supporting higher order learning and critical thinking through conversation and collaboration. Due to the greater level of</span><span lang="EN-GB"> data continuity, reliability, and privacy that LMSs can provide compared to the available free applications, LMSs are still the central platform for many universities to deliver e-learning. Therefore, it is vital to investigate the LMS structure requisites that affect user engagement. This paper focuses on the important LMS design factors that influence user engagement with e-learning tools within LMSs. Results were extracted from 74 interviews about Blackboard with students and lecturers within a major Australian university. </span>A user-friendly structure, avoidance of too many tools and links, support for privacy and anonymous posting, and more customisable student-centred tools were identified as LMS design factors that affect user engagement<span lang="EN-US">.</span></p>

This paper gives an overview of the INEX 2008 Ad Hoc Track. The main goals of the Ad Hoc Track were twofold. The first goal was to investigate the value of the internal document structure (as provided by the XML markup) for retrieving relevant information. This is a continuation of INEX 2007 and, for this reason, the retrieval results are liberalized to arbitrary passages and measures were chosen to fairly compare systems retrieving elements, ranges of elements, and arbitrary passages. The second goal was to compare focused retrieval to article retrieval more directly than in earlier years. For this reason, standard document retrieval rankings have been derived from all runs, and evaluated with standard measures. In addition, a set of queries targeting Wikipedia have been derived from a proxy log, and the runs are also evaluated against the clicked Wikipedia pages. The INEX 2008 Ad Hoc Track featured three tasks: For the Focused Task a ranked-list of nonoverlapping results (elements or passages) was needed. For the Relevant in Context Task non-overlapping results (elements or passages) were returned grouped by the article from which they came. For the Best in Context Task a single starting point (element start tag or passage start) for each article was needed. We discuss the results for the three tasks, and examine the relative effectiveness of element and passage retrieval. This is examined in the context of content only (CO, or Keyword) search as well as content and structure (CAS, or structured) search. Finally, we look at the ability of focused retrieval techniques to rank articles, using standard document retrieval techniques, both against the judged topics as well as against queries and clicks from a proxy log.

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