The Use of On-Line Computer adaptive Testing to raise Standardized Test Scores

Nursing school graduates are under pressure to pass the RN-NCLEX Exam on the first attempt since New York State monitors the results and uses them to evaluate the school’s nursing programs. Since the RN-NCLEX Exam is a standardized test, we sought a method to make our students better test takers. The use of on-line computer adaptive testing has raised our student’s standardized test scores at the end of the nursing course.

Development of on-line courses focusing on quality

The purpose of this paper is to show by which means quality in on-line education is achieved at Dalarna University. As a leading provider of online university courses in northern Europe, both in terms of number of students conducting their studies entirely on-line compared to the whole student body, (approximately 70% on-line students all subjects included), Dalarna University has acquired de facto extensive practical experience in the field of information technologies related to distance education. It has been deemed essential, to ensure that the quality of teaching reflects the principles governing the assessment of learning so that on-line education is deemed as comparative to campus education, both from a legal and cognitive point-of-view. Dalarna University began on-line courses in 2002 and it soon became clear that the interaction between the teacher and the student should make its mark in all stages of the learning process in order to both maintain the learners' motivation and ensure the assimilation of knowledge. We will illustrate these aspects by giving examples of what has been done in the recent years in on-line teaching of languages. As this method of teaching is not limited to learning basic language skills, but also to the study of literature, social issues and the language system of the various cultures, our presentation will offer a broad range of areas where the principles of quality in education are provided on a daily basis.

On-line learning in multilayer neural networks

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.

Using ancillary statistics in on-line learning algorithms

Neural networks are usually curved statistical models. They do not have finite dimensional sufficient statistics, so on-line learning on the model itself inevitably loses information. In this paper we propose a new scheme for training curved models, inspired by the ideas of ancillary statistics and adaptive critics. At each point estimate an auxiliary flat model (exponential family) is built to locally accommodate both the usual statistic (tangent to the model) and an ancillary statistic (normal to the model). The auxiliary model plays a role in determining credit assignment analogous to that played by an adaptive critic in solving temporal problems. The method is illustrated with the Cauchy model and the algorithm is proved to be asymptotically efficient.

Globally optimal parameters for on-line learning in multilayer neural networks

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

Globally optimal on-line learning rules for multi-layer neural networks

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison.

On-line learning with adaptive back-propagation in two-layer networks

An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, we analyse these learning algorithms in both the symmetric and the convergence phase for finite learning rates in the case of uncorrelated teachers of similar but arbitrary length T. These analyses show that adaptive back-propagation results generally in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Incorporating curvature information into on-line learning

We analyse the dynamics of a number of second order on-line learning algorithms training multi-layer neural networks, using the methods of statistical mechanics. We first consider on-line Newton's method, which is known to provide optimal asymptotic performance. We determine the asymptotic generalization error decay for a soft committee machine, which is shown to compare favourably with the result for standard gradient descent. Matrix momentum provides a practical approximation to this method by allowing an efficient inversion of the Hessian. We consider an idealized matrix momentum algorithm which requires access to the Hessian and find close correspondence with the dynamics of on-line Newton's method. In practice, the Hessian will not be known on-line and we therefore consider matrix momentum using a single example approximation to the Hessian. In this case good asymptotic performance may still be achieved, but the algorithm is now sensitive to parameter choice because of noise in the Hessian estimate. On-line Newton's method is not appropriate during the transient learning phase, since a suboptimal unstable fixed point of the gradient descent dynamics becomes stable for this algorithm. A principled alternative is to use Amari's natural gradient learning algorithm and we show how this method provides a significant reduction in learning time when compared to gradient descent, while retaining the asymptotic performance of on-line Newton's method.

Globally optimal on-line learning rules

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resulting rule can significantly outperform the locally optimal rule.

A Bayesian approach to on-line learning

Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we show that the algorithm achieves asymptotic efficiency. An application to learning in single layer neural networks is given.

Use of on-line liquid chromatography-nuclear magnetic resonance spectroscopy for the rapid investigation of flavonoids from Sorocea bomplandii

This paper reports the identification of di- and triglycosylated flavonoids from Sorocea bomplandii (Moraceae) by liquid chromatography coupled on-line to nuclear magnetic resonance (LC-NMR). These glycosylated flavonoids may be used as a taxonomic marker in future work. (C) 2002 Elsevier B.V. B.V All rights reserved.

Application of on-line C30 RP-HPLC-NMR for the analysis of flavonoids from leaf extract of Maytenus aquifolium

The application of on-line C30-reversed-phase high-pressure liquid chromatography-nuclear magnetic resonance spectroscopy is described for the analysis of tetraglycosylated flavonoids in aqueous and hydroalcoholic extracts of the leaves of Maytenus aquifolium (Celastraceae). Triacontyl stationary phases showed adequate separation for on-line 1H-NMR measurements at 600 MHz and allowed the characterisation of these flavonoids by detection of both aromatic and anomeric proton signals. Copyright (C) 2000 John Wiley and Sons, Ltd.