Designing corpus-based supplementary activities to promote motivation in the English classroom among 9th-grade learners in Ruila Basic School

Keeleõpe ei tähenda lihtsalt õpikust juhindumist. Selle uurimistöö eesmärgid on uurida sellist võtmetegurit nagu noorte õppijate motivatsioon inglise keele õppimiseks, välja selgitada nende vajadused, samuti koostada hetkel kasutusel oleva õpiku korpus ja luua korpusel põhinevad materjalid klassiruumis kasutamiseks. Küsimustikku, mida mõõdab ka motivatsiooni, kasutatakse selleks, et selgitada välja, mis motiveerib õppijaid, millised harjutused on nende meelest huvitavad ja kasulikud inglise keele õppimisel ning kas nad leiavad, et kasutatava õpiku tegevused on huvitavad ja motiveerivad neid piisavalt. Materjalide kavandamiseks luuakse, analüüsitakse ja kasutatakse õpiku tekstide korpust. Teine lühike küsimustik aitab tuvastada, mida õppijad arvavad äsja loodud materjalide kohta. Õpik pakub õppijatele sõnu, millega B1 tasemel inglise keele õppijad peaksid olema tuttavad vastavalt CEFR-le (Common European Framework of Reference for Languages – 101 Euroopa keeleõppe raamdokument) Seda võib pidada selle tugevaimaks küljeks. Õpiku lugemistekstid on rikkad ka akadeemilise sõnavara poolest. Aga kui asi puudutab kõige sagedamini kasutatavate ingliskeelsete sõnade levimist, pole tulemused rahuldavad. Näiteks nimisõnu, mida esineb lugemistekstides rohkem kui kümme korda, on õpikus tugevalt ülekasutatud, kui võrrelda COCA-ga (the Corpus of Contemporary American English – tänapäevase ameerika inglise keele korpus). Teiselt poolt on paljud tavalised leksikaalsed verbid alaesindatud, näiteks to say (ütlema), to know (teadma), to come (tulema), to want (tahtma) ja to tell (rääkima, ütlema). Samas on tuvastatud, et õpiku autorid on väga tähtsaks hinnanud arvukaid akadeemilisi sõnu. Tekstides on 143 akadeemilist sõna, mille levimine õpikus on palju tihedam kui korpuses. Mis puudutab mitmesõnalisi väljendeid (kollokatsioonid, ühendtegusõnad jne), siis annab õpik ainult piiratud valiku kollokatsioone leksikaliseerimata verbidega, kuid siiski on mitmesõnaliste väljendite esinemise sagedus õpikus palju suurem kui korpuses. Esmapilgul näib, et ka ühendtegusõnu on kasutatud üleliia, kuid paljud neist esinevad vaid korra, seega puutuvad õppijad nendega väga piiratult kokku.http://www.ester.ee/record=b5142545*es

Second language acquisition of the English interrogatives : the effect of different learning contexts on the SLA of three groups of Chinese learners of English.

SIGLEAvailable from British Library Document Supply Centre- DSC:D91701 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Looking into phrasal verbs

The phrasal verb is a unique type of verb phrase that consists of a main verb, usually of only one or two syllables, followed by a particle, that works as a single semantic unit. Such meaning, however, is characteristically expressed in idomatic terms, which poses a formidable problem for students of English as a second language. To be understood, this meaning must be figuratively interpreted as well as literally translated

The role of metalinguistic awareness in the development of a semiotic apprenticeship.

The current thesis explores the role played by metalinguistic awareness in language\ud acquisition/learning and seeks to develop a theoretical framework in which the integration of\ud `knowledge about language' [KALI in the school curriculum can be clarified in terms of curriculum\ud planning and pedagogic practice.\ud Chapter 1 unravels the confusion surrounding terms such as metalinguistic 'awareness' and/or\ud `consciousness' by relating them to the discussion on metacognition currently in vogue in cognitive\ud psychology. A taxonomy of theoretical models is considered.\ud Chapter 2 relates differences in the definition of metalinguistic awareness and its function in\ud language acquisition/learning to the theoretical models outlined in Chapter 1. A socio-cultural\ud viewpoint, it is argued, which views metalinguistic awareness arising out of the progressive\ud decontextualisation of functional variants, is the most useful in interpreting existing data.\ud Chapter 3 seeks to build on Chapter 2 by developing a more elaborated model of the role of\ud metalinguistic awareness in the emergence of 'parasitic' language skills within a socio-cultural\ud paradigm. The model examines the interdependence of skill and knowledge in the child's\ud expanding linguistic repertoire and suggests a taxonomy of 'meta' processes facilitating such an\ud expansion.\ud Chapter 4 addresses the variability of metalinguistic skills among children in terms of their\ud semiotic experience and, largely through a reconsideration of Bernstein's theory of codes, explores\ud the implications of such variability for educational development.\ud After a critical review of past practice, Chapter 5 proposes guidelines for the integration of\ud KAL into the curriculum based upon the notion of the learner as 'reflective practitioner'. Chapter 6\ud concretises this approach by seeking to link differences in pedagogy between L1 and L2, and within\ud L2 between second and foreign language learning, with differences in the extent of 'reflective\ud practice' required.\ud In conclusion, tentative suggestions are considered regarding the implications of such an\ud approach for Initial Teacher Education

Machine learning for corporate failure prediction : an empirical study of South African companies

Includes bibliographical references (leaves 255-266).The research objective of this study was to construct an empirical model for the prediction of corporate failure in South Africa through the application of machine learning techniques using information generally available to investors. The study began with a thorough review of the corporate failure literature, breaking the process of prediction model construction into the following steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE Securities Exchange between 1 January 1996 and 30 June 2003. A paired sample of non-failed companies was selected. Pairing was performed on the basis of year of failure, industry and asset size (total assets per the company financial statements excluding intangible assets). A minimum of two years and a maximum of three years of financial data were collated for each company. Such data was mainly sourced from BFA McGregor RAID Station, although the BFA McGregor Handbook and JSE Handbook were also consulted for certain data items. A total of 75 financial and non-financial ratios were calculated for each year of data collected for every company in the final sample. Two databases of ratios were created - one for all companies with at least two years of data and another for those companies with three years of data. Missing and undefined data items were rectified before all the ratios were normalised. The set of normalised values was then imported into MatLab Version 6 and input into a Population-Based Incremental Learning (PBIL) algorithm. PBIL was then used to identify those subsets of features that best separated the failed and non-failed data clusters for a one, two and three year forward forecast period. Thornton's Separability Index (SI) was used to evaluate the degree of separation achieved by each feature subset

The function of phrasal verbs and their lexical counterparts in technical manuals

Much recent attention has been devoted to the semantic, syntactic, and pragmatic properties of phrasal verbs--those two-part lexical items like put on and tighten up , along with suggestions regarding effective methods of teaching them to non-native speakers. According to Cornell (1985), phrasal verbs, have been \u27discovered\u27 as an important component in curricula for English as a Foreign Language (p. 1). However, it is very possible that they have become objects of current research primarily because of their complexity: their polysemy, their idiomaticity, their syntactic restraints, a complexity that means covering phrasal verbs in an ESL/EFL course can be a time-consuming process

A Bayesian framework for concept learning

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1999.Includes bibliographical references (p. 297-314).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples can provide a complete picture of how people generalize concepts in even this simple setting. This thesis proposes a new computational framework for understanding how people learn concepts from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more information about a concept's extension from a given set of observed examples than either rule-based or similarity-based approaches do, and can use this information in a rational way to infer the probability that any new object is also an instance of the concept. There are three components of the Bayesian framework: a prior probability distribution over a hypothesis space of possible concepts; a likelihood function, which scores each hypothesis according to its probability of generating the observed examples; and the principle of hypothesis averaging, under which the learner computes the probability of generalizing a concept to new objects by averaging the predictions of all hypotheses weighted by their posterior probability (proportional to the product of their priors and likelihoods). The likelihood, under the assumption of randomly sampled positive examples, embodies the size principle for scoring hypotheses: smaller consistent hypotheses are more likely than larger hypotheses, and they become exponentially more likely as the number of observed examples increases. The principle of hypothesis averaging allows the Bayesian framework to accommodate both rule-like and similarity-like generalization behavior, depending on how peaked the posterior probability is. Together, the size principle plus hypothesis averaging predict a convergence from similarity-like generalization (due to a broad posterior distribution) after very few examples are observed to rule-like generalization (due to a sharply peaked posterior distribution) after sufficiently many examples have been observed. The main contributions of this thesis are as follows. First and foremost, I show how it is possible for people to learn and generalize concepts from just one or a few positive examples (Chapter 2). Building on that understanding, I then present a series of case studies of simple concept learning situations where the Bayesian framework yields both qualitative and quantitative insights into the real behavior of human learners (Chapters 3-5). These cases each focus on a different learning domain. Chapter 3 looks at generalization in continuous feature spaces, a typical representation of objects in psychology and machine learning with the virtues of being analytically tractable and empirically accessible, but the downside of being highly abstract and artificial. Chapter 4 moves to the more natural domain of learning words for categories of objects and shows the relevance of the same phenomena and explanatory principles introduced in the more abstract setting of Chapters 1-3 for real-world learning tasks like this one. In each of these domains, both similarity-like and rule-like generalization emerge as special cases of the Bayesian framework in the limits of very few or very many examples, respectively. However, the transition from similarity to rules occurs much faster in the word learning domain than in the continuous feature space domain. I propose a Bayesian explanation of this difference in learning curves that places crucial importance on the density or sparsity of overlapping hypotheses in the learner's hypothesis space. To test this proposal, a third case study (Chapter 5) returns to the domain of number concepts, in which human learners possess a more complex body of prior knowledge that leads to a hypothesis space with both sparse and densely overlapping components. Here, the Bayesian theory predicts and human learners produce either rule-based or similarity-based generalization from a few examples, depending on the precise examples observed. I also discusses how several classic reasoning heuristics may be used to approximate the much more elaborate computations of Bayesian inference that this domain requires. In each of these case studies, I confront some of the classic questions of concept learning and induction: Is the acquisition of concepts driven mainly by pre-existing knowledge or the statistical force of our observations? Is generalization based primarily on abstract rules or similarity to exemplars? I argue that in almost all instances, the only reasonable answer to such questions is, Both. More importantly, I show how the Bayesian framework allows us to answer much more penetrating versions of these questions: How does prior knowledge interact with the observed examples to guide generalization? Why does generalization appear rule-based in some cases and similarity-based in others? Finally, Chapter 6 summarizes the major contributions in more detailed form and discusses how this work ts into the larger picture of contemporary research on human learning, thinking, and reasoning.by Joshua B. Tenenbaum.Ph.D