Supporting early oral language skills for English language learners in inner city preschool provision

BACKGROUND: A significant number of children now enter formal education in England with reduced levels of proficiency in oral language. Children who come from disadvantaged backgrounds and who are English language learners (ELL) are at risk of limited oral language skills in English which impacts on later educational achievement. AIMS: This paper reports the development of a theoretically motivated oral language intervention, Talking Time, designed to meet the needs of preschool children with poor language skills in typical preschool provision. SAMPLE: One hundred and forty-two 4-year-old children attending three inner city preschools in a disadvantaged area of London, England. METHOD: This is a quasi-experimental intervention study comparing children exposed to Talking Time with children exposed to a contrast intervention and children receiving the statutory early years curriculum. Measures were taken of both targeted and non-targeted language and cognitive skills. RESULTS: Data were analysed for the ELL. The intervention had a significant effect on vocabulary, oral comprehension, and sentence repetition but not narrative skills. As predicted, there were no effects on the skills which were not targeted. CONCLUSIONS: Regular evidence-based oral language interactions can make significant improvements in children's oral language. There is a need to examine the efficacy of more intensive interventions to raise language skills to allow learners to access the curriculum

Interactive task design: Metachat and the whole learner

In this chapter the focus is on conversations about language between adult learners online, in synchronous and asynchronous postings. Socio-affective and social-semiotic perspectives are used, thus distancing the work somewhat from cognitive ways of looking at tasks. Because adults come to the task with diverse knowledge of both L2 and L1, the expectation is that metalinguistic interaction will enable them to swap expert and novice roles with each other within the constantly changing dynamics of the classroom. This if shown to be the case would advance an educational agenda favouring learner-directedness. Secondly, as metalinguistic conversations develop in directions that the learners feel like following, a greater degree of contingency can arise. This is considered in this paper as motivational for adults, and also as progressive, following van Lier (1996: 180) for whom in a contingent conversation "the agenda is shared by all participants and educational reality may be transformed". However, in seeking to satisfy his condition of contingency, the problem of designing tasks for greater spontaneity proves difficult. Therefore this study provide an ethnographic account of metalinguistic conversations by learners engaged in an online task, Simuligne, designed to address this difficulty. After studying data from the project forums, chat rooms and emails, we introduce a new perspective on the function of these conversations, which holds pointers for task design

Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.Comment: 15 pages, 5 tables, 4 figure

Poverty and closing the gap: Adastra research consultancy projects

A fundamental principle which underpins all our teaching and research is the integration of theory and practice, and this principle is vividly exemplified within all the research consultancy projects. The longstanding successes in teacher training of Bishop Grosseteste University are founded upon the strength of our partnership with schools and educational settings. A key strand, that links our research ambitions with our commitment to teacher education, is the engagement in research consultancy and action research with partnership schools. The following reports all embed this approach of teachers working alongside researchers, integrating theory with practice, and focusing upon school-specific issues. They also represent excellent examples of how research can genuinely impact the prospects and life chances of young people. At the heart of our partnership is a focus on learners and learning, which the projects here also clearly share, alongside their key contribution in helping to close the educational attainment gap. Specifically, the research projects will enable the individual schools to develop further good practice, for the benefit of their own pupils, but also with potential applicability to other schools and settings. Furthermore, it is hoped they may motivate and inspire other teachers or schools to embark upon action research projects, driving further improvements in teaching and learning. Finally, for the individual teachers involved, the experience will hopefully stimulate an on-going theory-practice dialogue and provide impetus for further CPD and/or action research

Achievement for all: leadership matters

Purpose: "The focus of this document is on leadership - the impact that effective leaders have on their schools and their pupils; and the impact that the project has had upon the leadership styles and strategies within participating schools. It aims to: provide an overview of the key strands of the AfA project and give evidence of the impact it has had; identify characteristics of effective leadership that best support the achievement for all children and young people; share key learning from the project and provide illustrations of successful practice from participating schools; encourage other leaders to reflect on their own practice and to adopt AfA as a strategy for improving pupils' attainment and progress in their schools Throughout this document questions are suggested for schools to use to prompt discussion with the leadership group, staff, governors, parents and pupils, where appropriate." - Page 3

A retrieval-based dialogue system utilizing utterance and context embeddings

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.Comment: A shorter version is accepted at ICMLA2017 conference; acknowledgement added; typos correcte

Dialogue based interfaces for universal access.

Conversation provides an excellent means of communication for almost all people. Consequently, a conversational interface is an excellent mechanism for allowing people to interact with systems. Conversational systems are an active research area, but a wide range of systems can be developed with current technology. More sophisticated interfaces can take considerable effort, but simple interfaces can be developed quite rapidly. This paper gives an introduction to the current state of the art of conversational systems and interfaces. It describes a methodology for developing conversational interfaces and gives an example of an interface for a state benefits web site. The paper discusses how this interface could improve access for a wide range of people, and how further development of this interface would allow a larger range of people to use the system and give them more functionality