Evaluation of the quality and promotion of open learning materials

In the UK, Open Learning has been used in industrial training for at least the last decade. Trainers and Open Learning practitioners have been concerned about the quality of the products and services being delivered. The argument put forward in this thesis is that there is ambiguity amongst industrialists over the meanings of `Open Learning' and `Quality in Open Learning'. For clarity, a new definition of Open Learning is proposed which challenges the traditional learner-centred approach favoured by educationalists. It introduces the concept that there are benefits afforded to the trainer/employer/teacher as well as to the learner. This enables a focussed view of what quality in Open Learning really means. Having discussed these issues, a new quantitative method of evaluating Open Learning is proposed. This is based upon an assessment of the degree of compliance with which products meet Parts 1 & 2 of the Open Learning Code of Practice. The vehicle for these research studies has been a commercial contract commissioned by the Training Agency for the Engineering Industry Training Board (EITB) to examine the quality of Open Learning products supplied to the engineering industry. A major part of this research has been the application of the evaluation technique to a range of 67 Open Learning products (in eight subject areas). The findings were that good quality products can be found right across the price range - so can average and poor quality ones. The study also shows quite convincingly that there are good quality products to be found at less than 50. Finally the majority (24 out of 34) of the good quality products were text based.

Trainer talk:structures of interaction in teacher training classrooms

The subject of this research is interaction and language use in an institutional context, the teacher training classroom. Trainer talk is an interactional accomplishment and the research question is: what structures of talk-in-interaction characterise trainer talk in this institutional setting? While there has been research into other kinds of classroom and into other kinds of institutional talk, this study is the first on trainer discourse. The study takes a Conversation Analysis approach to studying institutional interaction and aims to identify the main structures of sequential organization that characterize teacher trainer talk as well as the tasks and identities that are accomplished in it. The research identifies three main interactional contexts in which trainer talk is done: expository, exploratory and experiential. It describes the main characteristics of each and how they relate to each other. Expository sequences are the predominant interactional contexts for trainer talk. But the research findings show that these contexts are flexible and open to the embedding of the other two contexts. All three contexts contribute to the main institutional goal of teaching teachers how to teach. Trainer identity is related to the different sequential contexts. Three main forms of identity in interaction are evidenced in the interactional contexts: the trainer as trainer, the trainer as teacher and the trainer as colleague. Each of them play an important role in teacher trainer pedagogy. The main features of trainer talk as a form of institutional talk are characterised by the following interactional properties: 1. Professional discourse is both the vehicle and object of instruction - the articulation of reflection on experience. 2. There is a reflexive relationship between pedagogy and interaction. 3. The professional discourse that is produced by trainees is not evaluated by trainers but, rather, reformulated to give it relevant precision in terms of accuracy and appropriacy.

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. © 2008. Licensed under the Creative Commons.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

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 had changed our editorial approach in drawing together the articles for this 2005/6 edition (our third) of the ABS Good Practice Guide. Firstly we have expanded our contributors beyond ABS academics. This year?s articles have also been written by staff from other areas of the University, a PhD student, a post-doctoral researcher and staff working in learning support. We see this as an acknowledgement that the learning environment involves a range of people in the process of student support. We have also expanded the maximum length of the articles from two to five pages, in order to allow greater reflection on the issues. The themes of the papers cluster around issues relating to diversity (widening participation and internationalisation of the student body), imaginative use of new technology (electronic reading on BlackboardTM ) and reflective practitioners, (reflection on rigour and relevance; on how best to train students in research ethics, relevance in the curriculum and the creativity of the teaching process) Discussion of efforts to train the HE teachers of the future looks forward to the next academic year when the Higher Education Academy?s professional standards will be introduced across the sector. In the last 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 is listed as an appendix to this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk) who coordinates the HELM seminars. HELM has also won its first independent grant from the EU Leonardo programme to look at the effect of business education on employment. In its annual report to the ABS Research Committee HELM listed for 2004 and 2005, 11 refereed journal articles, 4 book chapters, 3 published conference papers, 18 conference papers, one official reports and £72,500 of grant money produced in this research area across the School. I hope that this shows that reflection on learning is live and well in ABS. 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 our diverse approaches into a coherent and publishable form.

Intergroup relations in action: questions asked about lesbian, gay and bisexual issues in diversity training

This paper focuses on the questions which heterosexual trainees ask about lesbian, gay and bisexual (LGB) experience within diversity training about LGB issues. Drawing on a data corpus of 162 questions asked by trainees in 13 tape-recorded training sessions, questions were coded into six categories: (1) general understanding questions; (2) questions about the trainer's life, experience and practices; (3) professional practice questions; (4) questions about lesbian and gay related legislation, policies and procedures; (5) questions about specific people and projects and (6) questions about the meanings, derivations and correct use of terms and symbols. Real questions are compared with the decontexualized questions (and answers to them) that are provided in training manuals and it is demonstrated that these questions differ markedly from how questions actually get asked and how they actually get answered. Recommendations are provided for improving training and the argument made for turning towards analyses of the real world in action, especially when considering intergroup relations. Copyright © 2008 John Wiley & Sons, Ltd.

GTM through time

The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (iid) vectors. For time series, however, the iid assumption is a poor approximation. In this paper we show how the GTM algorithm can be extended to model time series by incorporating it as the emission density in a hidden Markov model. Since GTM has discrete hidden states we are able to find a tractable EM algorithm, based on the forward-backward algorithm, to train the model. We illustrate the performance of GTM through time using flight recorder data from a helicopter.

Causes of tension in post-observation feedback in pre-service teacher training: an alternative view

A central component in pre-service teacher training is teaching practice and feedback. In some cases, feedback results in disquiet and tension (Brandt, 2008). Many researchers attribute this tension to the incompatibility of the assessment and development roles that the trainer must perform. The research reported on here, however, suggests that tension may also be rooted in a difference in expectation amongst trainers and trainees about the purpose and performance of feedback. This can result in trainees not playing by the rules of the game (Roberts & Sarangi, 2001) either because they do not understand them or because they wish to challenge them.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Dopamine, empathy and the neurobiology of leadership

Objectives: Organisational Psychologists have long sought after methods by which to train individuals to become more effective leaders. Indeed considerable sums of money are spent on the design of such training programs. Yet it is not clear whether or not leadership skills can be taught or whether they are innate. Social leadership is a varied construct consisting of many diverse aspects, yet the ability to empathise with subordinates is a core skill that underpins effective transformational leadership. This type of leadership consists of four characteristics which are labelled ‘idealized influence’, ‘inspirational motivation’, ‘intellectual stimulation’ and ‘individualized consideration’. This is distinct from the transactional style of leadership, which is based on offering contingent rewards for completion of specific tasks. By identifying a specific gene that mediates distinct leadership traits, more effective training regimes can be designed. Design: There are two likely candidate genes that may mediate empathic leadership. The first is catechol-O-methyltransferase (COMT) which is involved with dopamine synthesis, and the second is the serotonin transporter promoter gene (5-HTTLPR). Both these genes mostly appear in the general population in their heterozygotic form. Thus by comparing phenotypes in leadership traits a measure of base line differences can be examined. Methods: 115 volunteers completed the Multifactor Leadership questionnaire (MLQ), which is a standard 12-item leadership psychometric scale and also underwent buccal swab for subsequent genotyping. Results: Of the 115 subjects 37 were heterozygotic for the COMT gene and 47 heterozygotic for 5-HTTLPR. Of the 12 MLQ subscales, the scores for two of the subscales only differed between the two participant groups. Individuals who were heterozygotic for the COMT gene scored higher on the ‘Inspirational motivation’ t(84)=1.99, p=0.05 and ‘Intellectual stimulation’ t(82)=1.94, p=0.05 scales compared to the carriers for the heterozygotic 5HTPP gene. Conclusions: Given that the behaviours described by these two MLQ subscales require leaders to empathise with subordinates, the current results suggest that dopamine may play a role in this important social task. The fact that both heterozygotic carriers for COMT and 5HTPP were compared allows a comparison to be made between the genotypes most prevalent in the general population.

Workplace interactions and the polymorphic role of e-mail

Purpose - One of the principal organizational developments in the last decade has been the pervasive influence of computer mediated communication (CMC) tools. The purpose of this paper is to closely interrogate the day-to-day role of e-mail in explicating, influencing and shaping social and information interactions within an organization. Design/methodology/approach - A series of in-depth interviews (n = 29) were undertaken to elicit employee opinions on their e-mail adaptation, experiences and practices. Findings - The paper provides insights into the polymorphic role of e-mail, particularly the way in which it is adapted by individuals within the organization. Specifically, it shows how this tool interacts within day-to-day work activities and tasks. Research limitations/implications - This paper investigates only one CMC tool, e-mail, although it is envisaged that this initial work will be used to raise a new understanding of the socially skilled adaptation of other CMC tools by employees as well as leaders. Practical implications- Previously unreported insights into employee opinion are delineated in order to provide a focus from which organizations can train and develop their employees and leaders to maximise knowledge creation within the organization. Originality/value - This study assesses CMC from an under-researched "real-life" perspective in which everyday interactions are used to understand employee reactions to e-mail communication and hence foster an atmosphere in which these interactions assist organizational development.

Relating two simultaneous events in discourse: the role of on-goingness devices in L1 Tunisian Arabic, L1 French and L2 French by Tunisian learners

Our PhD study focuses on the role of aspectual marking in expressing simultaneity of events in Tunisian Arabic as a first language, French as a first language, as well as in French as a second language by Tunisian learners at different acquisitional stages. We examine how the explicit markers of on-goingness qa:’id and «en train de» in Tunisian Arabic and in French respectively are used to express this temporal relation, in competition with the simple forms, the prefixed verb form in Tunisian Arabic and the présent de l’indicatif in French. We use a complex verbal task of retelling simultaneous events sharing an interval on the time axis based on eight videos presenting two situations happening in parallel. Two types of simultaneity are exploited: perfect simultaneity (when the two situations are parallel to each other) and inclusion (one situation is framed by the second one). Our informants in French and in Tunisian Arabic have two profiles, highly educated and low educated speakers. We show that the participants’ response to the retelling task varies according to their profiles, and so does their use of the on-goingness devices in the expression of simultaneity. The differences observed between the two profile groups are explained by the degree to which the speakers have developed a habit of responding to tasks. This is a skill typically acquired during schooling. We notice overall that the use of qa:’id as well as of «en train de» is less frequent in the data than the use of the simple forms. However, qa:’id as well as «en train de» are employed to play discursive roles that go beyond the proposition level. We postulate that despite the shared features between Tunisian Arabic and French regarding marking the concept of on-goingness, namely the presence of explicit lexical, not fully grammaticalised markers competing with other non-marked forms, the way they are used in the discourse of simultaneous events shows clear differences. We explain that «en train de» plays a more contrastive role than qa:’id and its use in discourse obeys a stricter rule. In cases of the inclusion type of simultaneity, it is used to construe the ‘framing’ event that encloses the second event. In construing perfectly simultaneneous events, and when both «en train de» and présent de l’indicatif are used, the proposition with «en train de» generally precedes the proposition with présent de l’indicatif, and not the other way around. qa:id obeys, but to a less strict rule as it can be used interchangeably with the simple form regardless of the order of propositions. The contrastive analysis of French L1 and L2 reveals learners’ deviations from natives’ use of on-goingness devices. They generalise the use of «en train de» and apply different rules to the interaction of the different marked and unmarked forms in discourse. Learners do not master its role in discourse even at advanced stages of acquisition despite its possible emergence around the basic and intermediate varieties. We conclude that the native speakers’ use of «en train de» involves mastering its role at the macro-structure level. This feature, not explicitly available to learners in the input, might persistently present a challenge to L2 acquisition of the periphrasis.

Lattice approach to the dynamics of phase-coded soliton trains

We address the collective dynamics of a soliton train propagating in a medium described by the nonlinear Schrödinger equation. Our approach uses the reduction of train dynamics to the discrete complex Toda chain (CTC) model for the evolution of parameters for each train constituent: such a simplification allows one to carry out an approximate analysis of the dynamics of positions and phases of individual interacting pulses. Here, we employ the CTC model to the problem which has relevance to the field of fibre optics communications where each binary digit of transmitted information is encoded via the phase difference between the two adjacent solitons. Our goal is to elucidate different scenarios of the train distortions and the subsequent information garbling caused solely by the intersoliton interactions. First, we examine how the structure of a given phase pattern affects the initial stage of the train dynamics and explain the general mechanisms for the appearance of unstable collective soliton modes. Then we further discuss the nonlinear regime concentrating on the dependence of the Lax scattering matrix on the input phase distribution; this allows one to classify typical features of the train evolution and determine the distance where the soliton escapes from its slot. In both cases, we demonstrate deep mathematical analogies with the classical theory of crystal lattice dynamics.