Once upon a time, there was a pulchritudinous princess…: the role of word definitions and multiple story contexts in children’s learning of difficult vocabulary

The close relationship between children’s vocabulary size and their later academic success has led researchers to explore how vocabulary development might be promoted during the early school years. We describe a study that explored the effectiveness of naturalistic classroom storytelling as an instrument for teaching new vocabulary to six- to nine-year-old children. We examined whether learning was facilitated by encountering new words in single versus multiple story contexts, or by the provision of age-appropriate definitions of words as they were encountered. Results showed that encountering words in stories on three occasions led to significant gains in word knowledge in children of all ages and abilities, and that learning was further enhanced across the board when teachers elaborated on the new words’ meanings by providing dictionary definitions. Our findings clarify how classroom storytelling activities can be a highly effective means of promoting vocabulary development.

New governance approaches to environmental regulation: an example of the Code for Sustainable Homes (CSH)

Environmental policy in the United Kingdom (UK) is witnessing a shift from command-and-control approaches towards more innovation-orientated environmental governance arrangements. These governance approaches are required which create institutions which support actors within a domain for learning not only about policy options, but also about their own interests and preferences. The need for construction actors to understand, engage and influence this process is critical to establishing policies which support innovation that satisfies each constituent’s needs. This capacity is particularly salient in an era where the expanding raft of environmental regulation is ushering in system-wide innovation in the construction sector. In this paper, the Code for Sustainable Homes (the Code) in the UK is used to demonstrate the emergence and operation of these new governance arrangements. The Code sets out a significant innovation challenge for the house-building sector with, for example, a requirement that all new houses must be zero-carbon by 2016. Drawing upon boundary organisation theory, the journey from the Code as a government aspiration, to the Code as a catalyst for the formation of the Zero Carbon Hub, a new institution, is traced and discussed. The case study reveals that the ZCH has demonstrated boundary organisation properties in its ability to be flexible to the needs and constraints of its constituent actors, yet robust enough to maintain and promote a common identity across regulation and industry boundaries.

Investigating orthographic and semantic aspects of word learning in poor comprehenders

This study compared orthographic and semantic aspects of word learning in children who differed in reading comprehension skill. Poor comprehenders and controls matched for age (9-10 years), nonverbal ability and decoding skill were trained to pronounce 20 visually presented nonwords, 10 in a consistent way and 10 in an inconsistent way. They then had an opportunity to infer the meanings of the new words from story context. Orthographic learning was measured in three ways: the number of trials taken to learn to pronounce nonwords correctly, orthographic choice and spelling. Across all measures, consistent items were easier than inconsistent items and poor comprehenders did not differ from control children. Semantic learning was assessed on three occasions, using a nonword-picture matching task. While poor comprehenders showed equivalent semantic learning to controls immediately after exposure to nonword meaning, this knowledge was not well retained over time. Results are discussed in terms of the language and reading skills of poor comprehenders and in relation to current models of reading development.

Flavonoids as modulators of memory and learning: molecular interactions resulting in behavioural effects

There is considerable interest in the potential of a group of dietary-derived phytochemicals known as flavonoids in modulating neuronal function and thereby influencing memory, learning and cognitive function. The present review begins by detailing the molecular events that underlie the acquisition and consolidation of new memories in the brain in order to provide a critical background to understanding the impact of flavonoid-rich diets or pure flavonoids on memory. Data suggests that despite limited brain bioavailability, dietary supplementation with flavonoid-rich foods, such as blueberry, green tea and Ginkgo biloba lead to significant reversals of age-related deficits on spatial memory and learning. Furthermore, animal and cellular studies suggest that the mechanisms underpinning their ability to induce improvements in memory are linked to the potential of absorbed flavonoids and their metabolites to interact with and modulate critical signalling pathways, transcription factors and gene and/or protein expression which control memory and learning processes in the hippocampus; the brain structure where spatial learning occurs. Overall, current evidence suggests that human translation of these animal investigations are warranted, as are further studies, to better understand the precise cause-and-effect relationship between flavonoid intake and cognitive outputs.

UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures

A better understanding of the systemic processes by which innovation occurs is useful, both conceptually and to inform policymaking in support of innovation in more sustainable technologies. This paper analyses current innovation systems in the UK for a range of new and renewable energy technologies, and generates policy recommendations for improving the effectiveness of these innovation systems. Although incentives are in place in the UK to encourage innovation in these technologies, system failures—or ‘gaps’—are identified in moving technologies along the innovation chain, preventing their successful commercialisation. Sustained investment will be needed for these technologies to achieve their potential. It is argued that a stable and consistent policy framework is required to help create the conditions for this. In particular, such a framework should be aimed at improving risk/reward ratios for demonstration and pre-commercial stage technologies. This would enhance positive expectations, stimulate learning effects leading to cost reductions, and increase the likelihood of successful commercialisation.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

(PDF, 406 KB) On L3 acquisition and phonological permeability: a new test case for debates on the mental representation of non-native phonological systems

The goal of this article is to make an epistemological and theoretical contribution to the nascent field of third language (L3) acquisition and show how examining L3 development can offer a unique view into longstanding debates within L2 acquisition theory. We offer the Phonological Permeability Hypothesis (PPH), which maintains that examining the development of an L3/Ln phonological system and its effects on a previously acquired L2 phonological system can inform contemporary debates regarding the mental constitution of postcritical period adult phonological acquisition. We discuss the predictions and functional significance of the PPH for adult SLA and multilingualism studies, detailing a methodology that examines the effects of acquiring Brazilian Portuguese on the Spanish phonological systems learned before and after the so-called critical period (i.e., comparing simultaneous versus successive adult English-Spanish bilinguals learning Brazilian Portuguese as an L3).

Breaking the impasse: towards a new regime for international climate governance

The reality of the current international order makes it imperative that a just and effective climate regime balance the historical responsibility of developed countries with the increasing absolute emissions from many developing nations. In this short outlook article, key pillars are proposed for a new international climate architecture that envisions replacing the current annex system with two new annexes –Annex α, for countries with high current emissions and historically high emissions, and Annex β, for countries with high current emissions and historically low emissions. Countries in both annexes would implement legally binding targets under this framework. Additionally, this proposal includes tweaks and revisions to funding and technology transfer mechanisms to correct for weaknesses and inequities under the current Kyoto architecture. The proposed framework stems from a belief that a top-down, international approach to climate policy remains the most effective for ensuring environmental integrity. Given the slow rate of institutional learning, reforming and improving the current system is held as a more efficient course of action than abandoning the progress already achieved. It is argued that the proposed framework effectively accommodates key equity, environmental integrity and political feasibility concerns.

The M curve and the performance of Spanish international new ventures

In this paper we address three challenges. First, we discuss how international new ventures (INVs) are probably not explained by the Uppsala model as there is no time for learning about foreign markets in newly born and small firms. Only in the longer term can INVs develop experiential learning to overcome the liability of foreignness as they expand abroad. Second, we advance theoretically on previous research demonstrating that the multinationality−performance relationship of INVs follows a traditional S-shaped relationship, but they first experience a ‘born global illusion’ which leads to a non-traditional M curve. Third, using a panel data analysis for the period 1994–2008 we find empirically that Spanish INVs follow an inverted U curve in the very short term, where no learning takes place, but that experience gained over time yields an M-curve relationship once learning takes place.

New wine into old skins: the enactment of literacy policy in Singapore

To better comprehend how educational reforms and classroom practice interconnect, we need to understand the epistemic environments created for learning, as well as the pedagogical activities and the modes of classroom discourse related to these activities. This article examines how a particular innovation in English literacy, Strategies for English Language Learning and Reading (STELLAR), has been implemented in Singapore. Outlining the broader curriculum initiatives, current literacy policy landscape and pedagogical effect of classroom discourse, we look at how English language teachers in grades 1 and 2 interpret the STELLAR curriculum. Situated within the larger international zeal of educational reform, Singapore presents a rich case for the study of policy–pedagogy initiatives, literacy instruction and cultural values. Adding to the existing policy enactment research, this investigation provides an opportunity to probe both the prospects and limitations of policy implementation associated with centralised educational structures, examination-oriented systems and societal cultural frameworks.

Updating existing emotional memories involves the frontopolar/orbitofrontal cortex in ways that acquiring new emotional memories does not

In life, we must often learn new associations to people, places, or things we already know. The current fMRI study investigated the neural mechanisms underlying emotional memory updating. Nineteen participants first viewed negative and neutral pictures and learned associations between those pictures and other neutral stimuli, such as neutral objects and encoding tasks. This initial learning phase was followed by a memory updating phase, during which participants learned picture-location associations for old pictures (i.e., pictures previously associated with other neutral stimuli) and new pictures (i.e., pictures not seen in the first phase). There was greater frontopolar/orbito-frontal (OFC) activity when people learned picture–location associations for old negative pictures than for new negative pictures, but frontopolar OFC activity did not significantly differ during learning locations of old versus new neutral pictures. In addition, frontopolar activity was more negatively correlated with the amygdala when participants learned picture–location associations for old negative pictures than for new negative or old neutral pictures. Past studies revealed that the frontopolar OFC allows for updating the affective values of stimuli in reversal learning or extinction of conditioning [e.g., Izquierdo, A., & Murray, E. A. Opposing effects of amygdala and orbital PFC lesions on the extinction of instrumental responding in macaque monkeys. European Journal of Neuroscience, 22, 2341–2346, 2005]; our findings suggest that it plays a more general role in updating associations to emotional stimuli.

Linguistic relativity in SLA: towards a new research programme

The purpose of the current article is to support the investigation of linguistic relativity in second language acquisition and sketch methodological and theoretical prerequisites toward developing the domain into a full research program. We identify and discuss three theoretical-methodological components that we believe are needed to succeed in this enterprise. First, we highlight the importance of using nonverbal methods to study linguistic relativity effects in second language (L2) speakers. The use of nonverbal tasks is necessary in order to avoid the circularity that arises when inferences about nonverbal behavior are made on the basis of verbal evidence alone. Second, we identify and delineate the likely cognitive mechanisms underpinning cognitive restructuring in L2 speakers by introducing the theoretical framework of associative learning. By doing so, we demonstrate that the extent and nature of cognitive restructuring in L2 speakers is essentially a function of variation in individual learners’ trajectories. Third, we offer an in-depth discussion of the factors (e.g., L2 proficiency and L2 use) that characterize those trajectories, anchoring them to the framework of associative learning, and reinterpreting their relative strength in predicting L2 speaker cognition

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Modelling as learning: a consultancy methodology for enhancing learning in management teams

This article reviews the experiences of a practising business consultancy division. It discusses the reasons for the failure of the traditional, expert consultancy approach and states the requirements for a more suitable consultancy methodology. An approach called ‘Modelling as Learning’ is introduced, its three defining aspects being: client ownership of all analytical work performed, consultant acting as facilitator and sensitivity to soft issues within and surrounding a problem. The goal of such an approach is set as the acceleration of the client's learning about the business. The tools that are used within this methodological framework are discussed and some case studies of the methodology are presented. It is argued that a learning experience was necessary before arriving at the new methodology but that it is now a valuable and significant component of the division's work.