Effective literacy teaching practices in Portugal: A study in first grade.

This article examines the effect of literacy teaching practices on the reading ability of first grade pupils in Portuguese, a semi-transparent orthography. First grade teachers (N=267) self-reported their literacy teaching practices through a questionnaire. Hierarchical cluster analysis revealed three groups with different practices – Language Experience, Phonic, and Balanced. Eight teachers from each group were randomly selected for classroom observation (N=24) to gain more in-depth information about their practices, namely by analysing classroom management procedures and materials used. Their pupils’ reading abilities were assessed at the beginning and end of the first grade (N=465) through two tasks: word reading and comprehension. Multivariate analysis of covariance, controlling for mother’s educational levels, showed that pupils of balanced teachers had better results than pupils in the other two groups. These results are in line with those described in the English literature, pointing out that the key term for describing successful literacy teaching practices is balance: balance in classroom management procedures, from more teacher-centred to more pupil-centred; balance in different types of reading materials, from more authentic materials to materials designed to work on specific skills; and balance between explicit instruction in grapheme-phoneme correspondences and reading and writing authentic texts.info:eu-repo/semantics/publishedVersio

Effects of a three-pronged intervention on oral reading fluency and reading achievement: a case study

The purpose of this case study was to examine the effects of a three-pronged literacy intervention incorporating strategies and instruction in word identification, decoding by syllable, and oral reading practice on oral reading fluency and overall reading achievement. The participant was an eight year old male student struggling in reading achievement, particularly in oral reading fluency. The intervention followed a two day rotation utilizing a gradual release of responsibility instructional model. Several elements of the intervention incorporated the use of instructional technology. Multiple forms of data were collected, including preintervention data, pretest to posttest data, qualitative data, and ongoing progress monitoring. These data measured oral reading rate and accuracy, reading comprehension, word identification, and word attack. Statistically significant progress was found in all literacy areas noted except for the pretest to posttest oral reading fluency rate. Recommendations include emphasizing successive readings of the same text, using voice recording technology, emphasizing the parent-teacher relationship, and decreasing the participant’s reliance upon external reinforcement

The Different Faces of Reading Disabilities: Evidence from Case Studies

Children with language or phonological disorders, including those with motor speech disorders such as Childhood Apraxia of Speech (CAS), are all at risk for reading disorders. Other children who have neither known risk factors nor prior linguistic or learning difficulties have reading disorders nonetheless. Few studies have focused on specific differences in the reading profiles of children from these varied groups. These issues are explored in two case studies that highlight the differences between phonological dyslexia and literacy-related deficits that result from other conditions. The results show core differences between the two children's profiles, with the seemingly more impaired child demonstrating strengths in some relevant sub-skills in comparison to the otherwise higher-functioning girl. We stress the importance of identification and classification of reading disorders in order to provide appropriate remediation and improve chances in achieving literacy. Key words: Childhood Apraxia Of Speech; Phonological Dyslexia; Reading Deficits; Phonological Awareness; Phonological Memory; Working Memor

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on Autonomous Mental Development (TAMD

The Unicode cookbook for linguists: Managing writing systems using orthography profiles

This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research. This book is a prime example of open publishing as envisioned by Language Science Press. It is open access, has accompanying open source software, has open peer review, versioning and so on. Read more in this blog post. The book is continuously being improved. You can follow the development on https://github.com/unicode-cookbook/cookbook/releases/latest   &nbsp

The Unicode cookbook for linguists: Managing writing systems using orthography profiles

This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research. This book is a prime example of open publishing as envisioned by Language Science Press. It is open access, has accompanying open source software, has open peer review, versioning and so on. Read more in this blog post. The book is continuously being improved. You can follow the development on https://github.com/unicode-cookbook/cookbook/releases/latest   &nbsp