Impaired memory and olfactory performance in NaSi-1 sulphate transporter deficient mice

In the present study, NaSi-l sulphate transporter knock-out (Nas1-/-) mice, an animal model of hyposulphataernia, were examined for spatial memory and learning in a Morris water maze, and for olfactory function in a cookie test. The Nas1-/- mice displayed significantly (P < 0.05) increased latencies to find an escape platform in the reversal teaming trials at 2 days but not 1 day after the last acquisition trial in a Morris water maze test. suggesting that Nas1-/- mice may have proactive memory interference. While the wild-type (Ncis1+/+) mice showed a significant (P < 0.02) decrease in time to locate a hidden food reward over four trials after overnight fasting, Nas1-/- mice did not change their performance, resulting in significantly (P < 0.05) higher latencies when compared to their Nas1+/+ littermates. There were no significant differences between Nas1-/- and Nas1+/+ mice in the cookie test after moderate food deprivation. In addition, both Nas1-/- and Nas1+/+ mice displayed similar escape latencies in the acquisition phase of the Morris water maze test, suggesting that learning, motivation, vision and motor skills required for the task may not be affected in Nas1-/- mice. This is the first study to demonstrate an impairment in memory and olfactory performance in the hyposulphataemic Nas1-/- mouse. (c) 2004 Elsevier B.V. All rights reserved.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

An examination of learning processes during critical incident training: Implications for the development of adaptable trainees

Three experiments are reported that examined the process by which trainees learn decision-making skills during a critical incident training program. Formal theories of category learning were used to identify two processes that may be responsible for the acquisition of decision-making skills: rule learning and exemplar learning. Experiments I and 2 used the process dissociation procedure (L. L. Jacoby, 1998) to evaluate the contribution of these processes to performance. The results suggest that trainees used a mixture of rule and exemplar learning. Furthermore, these learning processes were influenced by different aspects of training structure and design. The goal of Experiment 3 was to develop training techniques that enable trainees to use a rule adaptively. Trainees were tested on cases that represented exceptions to the rule. Unexpectedly, the results suggest that providing general instruction regarding the kinds of conditions in which a decision rule does not apply caused them to fixate on the specific conditions mentioned and impaired their ability to identify other conditions in which the rule might not apply. The theoretical, methodological, and practical implications of the results are discussed.

Comparative academic performance of medical students in rural and urban clinical settings

OBJECTIVE To determine whether the academic performance of medical students learning in rural settings differs from those learning in urban settings. DESIGN Comparison of results of assessment for 2 full cohorts and 1 part cohort of medical students learning in rural and urban settings in 2002 (209 students), 2003 (226 students) and 2004 (220 students), including results for each specialist rotation in the 3rd year and end-of-year examinations in the 2nd and 4th years. SETTING University of Queensland School of Medicine, Brisbane. Students spent the whole 3rd year (of a 4-year graduate entry programme) conducting 5 specialist 8-week rotations in either the rural clinical division (rural students) or in Brisbane (urban students), all following the same curriculum and taking the same examinations. RESULTS For the 2002 cohort there were no statistically significant differences in academic performance between rural and urban students. For the 2003 cohort the only significant difference was a higher score for rural students in the end of the 4th-year clinical skills examination (65.7 versus 62.3%, P = 0.025). For the 2004 cohort, rural students scored higher in the 3rd-year mental health rotation (79.3 versus 76.2%, P = 0.038) and lower in the medicine rotation (65.5 versus 68.6%, P = 0.037). CONCLUSION Academic performance among students studying in rural and urban settings is comparable.

A class of self-stabilizing MCA learning algorithms

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

The effects of domain-specific avoid and prove goal orientations on performance: A multilevel analysis

Recently, goal orientation, a mental framework for understanding how individuals approach learning and achievement situadons, has emerged as an important predictor of performance. This study addressed the effects of domain-specific avoid and prove orientations on performance from the betweenand within-person levels of analysis. One hundred and three participants performed thirty trials of an airtraffic control task. Domain-specific avoid and prove orientations were measured before each trial to assess the effects of changes in goal orientadon on changes in performance (i.e. within-person relationships). Average levels of avoid and prove orientations were calculated to assess the effect of goal orientation on overall performance (i.e. between-person relationships). Findings from the between-person level of analysis revealed that high prove-orientated individuals performed better than low proveorientated individuals. Results also revealed that average goal orientation levels moderated the withinperson relationships. The effect of changes in avoid orientation on changes in performance was stronger for low versus high avoid-oriented individuals while the effect of changes in prove orientadon on changes in performances was stronger for low versus highprove oriented individuals. Implications of these findings are considered.

Dynamic iterative ontology learning

The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Time trials on second-order and variable-learning-rate algorithms

The performance of seven minimization algorithms are compared on five neural network problems. These include a variable-step-size algorithm, conjugate gradient, and several methods with explicit analytic or numerical approximations to the Hessian.

Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We have again changed our approach for this 2007/08 edition (our fifth) of the Aston Business School Good Practice Guide. As before, some contributions were selected from those identifying interesting best practice on their Annual Module Reflection Forms in 2006/2007. Brookes? contribution this year is directly from her annual reflection. Other contributors received HELM (Research Centre in Higher Education Learning and Management) small research grants in 2006/2007. Part of the conditions were for them to write an article for this publication. We have also been less tight on the length of the articles this year. Some contributions are, therefore, on the way to being journal articles. HELM will be working with these authors to help develop these for publication. Looking back over the last five years it is brilliant to see how many different people have contributed over the years and, therefore, how much innovative learning and teaching work has been taking place in ABS over this time. In the first edition we were just pleased for people to write a few pages on their teaching. Now things have changed dramatically. The majority of the articles are grounded in empirical research (some funded by HELM small research grants) and Palmer?s article was produced as part of the University?s Postgraduate Certificate in Learning and Teaching. Most encouraging of all, four of this year?s articles have since been developed further and submitted to refereed journals. We await news of publication as we go to press. It is not surprising that how to manage large groups still remains a central theme of the articles, ABS has a large and still growing student body. Essex and Simpson have looked at trying to encourage students to attend taught sessions, on the basis that there is a strong correlation between attendance and higher performance. Their findings are forming the platform of a further study currently being carried out in the Undergraduate Programme. A number of the other articles concentrate on trying to encourage students to engage with study in an innovative way. This is particularly obvious in Shaw?s work. Everyone who has been around campus lately has had evidence that the students on Duncan?s modules have clearly been inspired. I found myself, for example, playing golf in the student dining room as part of this initiative! The articles by Jarzabkowski & Guilietti and Ho involved much larger surveys. This is another first for the Good Practice Guide and marks the first step on what will clearly be larger research efforts for these authors in this area. We look forward to the journal publications which will result from this work. The last articles are the result of HELM?s hosting of the national conference of the Higher Education Academy?s Business, Management, Accounting and Finance (BMAF) Subject Centre Conference in May 2007. Belal and Foster have written about their impressions of the Conference and Andrews has included the paper she gave. The papers on employability and widening participation are the centre of HELM?s current work. In the second volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars for 2007/2008 is listed as Appendix 1 of this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk), who coordinates the HELM seminars. We have also been working on a list of target journals to guide ABS staff who wish to publish in this area. These are included as Appendix 2 of this publication. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting the varying diverse approaches into a coherent and publishable form and for agreeing to fund the printing of this volume.