The UK MPharm (1) - are we over reliant on traditional teaching methods?

Introduction-The design of the UK MPharm curriculum is driven by the Royal Pharmaceutical Society of Great Britain (RPSGB) accreditation process and the EU directive (85/432/EEC).[1] Although the RPSGB is informed about teaching activity in UK Schools of Pharmacy (SOPs), there is no database which aggregates information to provide the whole picture of pharmacy education within the UK. The aim of the teaching, learning and assessment study [2] was to document and map current programmes in the 16 established SOPs. Recent developments in programme delivery have resulted in a focus on deep learning (for example, through problem based learning approaches) and on being more student centred and less didactic through lectures. The specific objectives of this part of the study were (a) to quantify the content and modes of delivery of material as described in course documentation and (b) having categorised the range of teaching methods, ask students to rate how important they perceived each one for their own learning (using a three point Likert scale: very important, fairly important or not important). Material and methods-The study design compared three datasets: (1) quantitative course document review, (2) qualitative staff interview and (3) quantitative student self completion survey. All 16 SOPs provided a set of their undergraduate course documentation for the year 2003/4. The documentation variables were entered into Excel tables. A self-completion questionnaire was administered to all year four undergraduates, using a pragmatic mixture of methods, (n=1847) in 15 SOPs within Great Britain. The survey data were analysed (n=741) using SPSS, excluding non-UK students who may have undertaken part of their studies within a non-UK university. Results and discussion-Interviews showed that individual teachers and course module leaders determine the choice of teaching methods used. Content review of the documentary evidence showed that 51% of the taught element of the course was delivered using lectures, 31% using practicals (includes computer aided learning) and 18% small group or interactive teaching. There was high uniformity across the schools for the first three years; variation in the final year was due to the project. The average number of hours per year across 15 schools (data for one school were not available) was: year 1: 408 hours; year 2: 401 hours; year 3: 387 hours; year 4: 401 hours. The survey showed that students perceived lectures to be the most important method of teaching after dispensing or clinical practicals. Taking the very important rating only: 94% (n=694) dispensing or clinical practicals; 75% (n=558) lectures; 52% (n=386) workshops, 50% (n=369) tutorials, 43% (n=318) directed study. Scientific laboratory practices were rated very important by only 31% (n=227). The study shows that teaching of pharmacy to undergraduates in the UK is still essentially didactic through a high proportion of formal lectures and with high levels of staff-student contact. Schools consider lectures still to be the most cost effective means of delivering the core syllabus to large cohorts of students. However, this does limit the scope for any optionality within teaching, the scope for small group work is reduced as is the opportunity to develop multi-professional learning or practice placements. Although novel teaching and learning techniques such as e-learning have expanded considerably over the past decade, schools of pharmacy have concentrated on lectures as the best way of coping with the huge expansion in student numbers. References [1] Council Directive. Concerning the coordination of provisions laid down by law, regulation or administrative action in respect of certain activities in the field of pharmacy. Official Journal of the European Communities 1985;85/432/EEC. [2] Wilson K, Jesson J, Langley C, Clarke L, Hatfield K. MPharm Programmes: Where are we now? Report commissioned by the Pharmacy Practice Research Trust., 2005.

Quantifying uncertainty in citizen weather data

The sheer volume of citizen weather data collected and uploaded to online data hubs is immense. However as with any citizen data it is difficult to assess the accuracy of the measurements. Within this project we quantify just how much data is available, where it comes from, the frequency at which it is collected, and the types of automatic weather stations being used. We also list the numerous possible sources of error and uncertainty within citizen weather observations before showing evidence of such effects in real data. A thorough intercomparison field study was conducted, testing popular models of citizen weather stations. From this study we were able to parameterise key sources of bias. Most significantly the project develops a complete quality control system through which citizen air temperature observations can be passed. The structure of this system was heavily informed by the results of the field study. Using a Bayesian framework the system learns and updates its estimates of the calibration and radiation-induced biases inherent to each station. We then show the benefit of correcting for these learnt biases over using the original uncorrected data. The system also attaches an uncertainty estimate to each observation, which would provide real world applications that choose to incorporate such observations with a measure on which they may base their confidence in the data. The system relies on interpolated temperature and radiation observations from neighbouring professional weather stations for which a Bayesian regression model is used. We recognise some of the assumptions and flaws of the developed system and suggest further work that needs to be done to bring it to an operational setting. Such a system will hopefully allow applications to leverage the additional value citizen weather data brings to longstanding professional observing networks.

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.

Dynamics of on-line gradient descent learning for multilayer neural networks

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

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.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm 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, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Learning with noise and regularizers in multilayer neural networks

We study the effect of two types of noise, data noise and model noise, in an on-line gradient-descent learning scenario for general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labeled by a two-layer teacher network with an arbitrary number of hidden units. Data is then corrupted by Gaussian noise affecting either the output or the model itself. We examine the effect of both types of noise on the evolution of order parameters and the generalization error in various phases of the learning process.

Finite-size effects in on-line learning of multilayer neural networks

We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time.

Learning with regularizers in multilayer neural networks

We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.

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.

The role of biases in on-line learning of two-layer networks

The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase which has often been predominant in the original model all but disappears for a non-degenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g. attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Dynamics of on-line learning in radial basis function networks

On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.

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.

On-line learning in radial basis functions networks

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives insight into decreasing the time required for training. The realizable and over-realizable cases are studied in detail; the phase of learning in which the hidden units are unspecialized (symmetric phase) and the phase in which asymptotic convergence occurs are analyzed, and their typical properties found. Finally, simulations are performed which strongly confirm the analytic results.