Cette thèse vise à contribuer à l’approche DDL (Data-Driven Learning) dans l’enseignement du lexique en FLE. Dans le cadre de l’approche DDL, on fait appel aux corpus pour enseigner différentes composantes d’une langue. Le lexique, étant un des premiers besoins d’un apprenant, car il donne accès à la communication en langue étrangère, fait l’objet de nombreux travaux de recherche actuels en linguistique et en didactique. L’idiomaticité, trait constitutif de toutes les langues, se manifeste sous forme d’expressions variées. Elle relève du champ de la lexicologie, et plus spécifiquement de la phraséologie. La linguistique de corpus permet d’observer ce fait de langue dans un cadre structure/sens. Les expressions idiomatiques en général, et en particulier les collocations, sont mises au centre de la démarche didactique dans cette thèse. Les collocations à verbe support restent une source d’erreurs importante même aux niveaux avancés d’apprentissage. Le matériel didactique présenté aux lecteurs de cette étude cherche à promouvoir l’exploitation directe des corpus bilingues par les apprenants en classe afin d’identifier ces collocations en L1 et en L2, de les comprendre, de trouver des correspondances et de les employer de manière appropriée. L’approche comparative français-russe renforcée par une observation des lignes de concordance issues de corpus authentiques devraient permettre une meilleure acquisition des faits linguistiques visés. Ce travail s’inscrit dans une perspective d’apprentissage déductif et d’autonomisation des apprenants. / This thesis aims to contribute to the DDL (Data-Driven Learning) approach in French vocabulary teaching. In the framework of the DDL approach we use corpora to teach different language phenomena. Vocabulary, one of the immediate needs of a language learner because it makes a communication in a foreign language possible, has become a popular research theme in the fields of linguistics and language teaching. Idiomaticity, an inherent part of all languages, manifests through various expressions. Phraseology studies different ways of expressing idiomaticity. Corpus linguistics permits to observe this language phenomenon in a structure/sense framework. Idiomatic expressions in general and collocations in particular are the heart and the main focus of the teaching perspective described in this thesis. Even advanced language learners make errors in light verb constructions. The teaching material presented in this study seeks to promote the search in bilingual corpora in the classroom in order to identify these collocations in L1 and in L2, to understand them, to find equivalents and finally, to use them correctly. A comparative French-Russian approach reinforced by a study of concordance lines from authentic corpora might contribute to better understanding of a particular language feature. This study falls in line with deductive learning practices and with the learners’ autonomisation perspective.

abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are ap- plied that (1) allow for the decision-maker to express a spectrum of pessimism against model uncertainties while (2) still utilizing incoming data for learning. Two main ap- plications are investigated with respect to these goals, the first being a percentile optimization technique with respect to a multi-class queueing system for application in hospital Emergency Departments. The second studies the use of robust forecasting techniques in improving developing countries’ vaccine supply chains via (1) an inno- vative outside of cold chain policy and (2) a district-managed approach to inventory control. Both of these research application areas utilize data-driven approaches that feature learning and pessimism-controlled robustness. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2018

The purpose of this master's project was to write a resource book that demonstrates how teachers can use data-driven learning methods to teach vocabulary. First, a brief overview of corpus linguistics, data-driven learning, and the corpus used in this book (COCA) is given. Then, the book presents different aspects of vocabulary learning in the context of a corpus. Topics included are frequency knowledge, part of speech knowledge, morphological knowledge, synonym knowledge, collocational knowledge, and register knowledge with a chapter on each topic. For each aspect of vocabulary learning there is a section that introduces the topic to the teacher, followed by instructions on performing topic related searches in the corpus. Each chapter also includes examples and ideas for application to the vocabulary classroom. Additional chapters provide information on individual language learning, and an evaluation of the project. The goal of this project was to provide teachers with specific knowledge of vocabulary and corpus-linguistics to be able to teach less-frequently addressed aspects of vocabulary instruction and to encourage more use of corpora in the language classroom. It is hoped that after reading this book, teachers will be able to improve their vocabulary teaching and ability to use the Corpus of Contemporary American English and DDL methods in the ESL/EFL classroom. The evaluation of this project will consist of teacher reviews of the book after reading. Specifically, the questionnaire addresses readers' feelings of increased knowledge and understanding of these areas and desire to use them in the classroom.

Thesis (M.A.)--Indiana University, 2009. / Title from screen (viewed on February 1, 2009). Department of English, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Julie A. Belz, Ulla M. Connor, Thomas A. Upton. Includes vitae. Includes bibliographical references (leaves 65-67).

<p dir="ltr">Systems biology dynamic models, which are based on differential equations, offer a flexible and accurate framework to explain physiological properties emerging from complex biochem- ical or biological systems. These models enable explicit quantification and interpretation, allowing for simulation and perturbation analysis to study biological features and their inter- actions, as well as understanding system progression and convergence under various initial conditions. However, their application in human disease systems is limited due to unknown kinetics parameters under disease conditions and a reductionist paradigm that fails to cap- ture the complexity of diseases. Meanwhile, the advent of omics technologies provides high- resolution molecular measurements from single cells and spatially resolved samples, as well as comprehensive disease-specific molecular signatures from large patient cohorts. This wealth of data holds the promise for characterizing complex biological systems, necessitating ad- vanced systems biology models and computational tools that can harness multi-omics data to reliably depict biological processes. However, this endeavor faces the challenge of nonlinear relationships between omics data and the system’s dynamic properties, such as the global or local low-rank gene expression patterns across cell types and the nonlinear complexities within transcriptional regulatory networks revealed by single-cell RNA sequencing.</p><p dir="ltr">The overall goal of this report is to develop new computational frameworks, AI-empowered methods, and related mathematical theories to explicitly represent and approximate the dy- namics of complex biological systems by using biological omics data. Our aim is to unravel the intricacies of context-specific dynamic systems using multi-Omics data. Specifically, we solved two different but related computational tasks and enabled the first-of-its-kind methods to (1) identify local low-rank matrices from large omics data, and (2) a robust optimization strategy to approximate metabolic flux. Subsequently, we delve into the realm of data-driven and AI-powered systems biology, harnessing the power of statistical learning and artificial intelligence to approximate differential equations or their representations. This research en- deavor not only contributes to the advancement of subspace modeling but also offers insights into a wide array of complex phenomena across diverse domains, with profound implications for computational biology and beyond.</p>

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24146号 / 工博第5033号 / 新制||工||1786(附属図書館) / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 松野 文俊, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Indiana University-Purdue University Indianapolis (IUPUI)

Data-driven learning is a sub-discipline of corpus linguistics that makes use of the analyses and tools of corpus linguistics in foreign and second language classroom (Johns, 1991; Johns & King, 1991). With this approach, learners become researchers rather than passive recipients of language rules (Johns, 1991). This study was an investigation of the impact of this approach as a form of written corrective feedback for in-service teachers of English participating in an online writing course at a teacher training institute in Japan. Data-driven learning is commonly utilized in conventional, face-to-face classrooms, or computer lab settings in which there is close direction from the instructor on how to interpret the output of a corpus query. The purpose of this study was to investigate how data-driven learning can be implemented in a blended online environment by providing training to develop the participants’ corpus competence (Charles, 2011; Flowerdew, 2010), which is defined as the ability to interpret data obtained from querying a corpus. This competence has been associated with becoming familiar with corpus methods, which include interpreting concordances, and in turn can aid in accurately repairing writing errors. This training, while initially presented in a face-to-face session at the beginning of the course, was sustained with support from resources on the course’s Moodle website and my comments in Microsoft Word documents. In addition, I applied a fine-grained approach to the analysis of the to examine the quality of participants’ interpretation of concordances. The mixed method triangulation convergence design (Creswell & Plano Clark, 2007, 2011) used in this study was based on data from four sources to examine the effectiveness of data-driven learning in an online environment as well as to observe how the participants interpreted concordances. One data set involved an analysis of the participants’ responses in drafts of their own writing to concordance-based feedback. The participants were given a prefabricated concordance, which was a concordance I generated. That concordance was attached to an error in the participants’ document and the participants used the information provided by the concordance to repair their writing error. The resulting data set, which contains the concordance, along with before and after comparisons of the writers’ repairs, shows how the participants’ interpretations of concordances aided the repairs. With the evidence of several trials over the course of four writing assignments, it was possible to see how the participants used the supplied concordance to repair their writing errors and in turn revealed their degree of corpus competence. A second data set obtained from think-aloud protocols from select participants was utilized to reveal how they interpreted the concordance during an error-repair task. This data revealed what kind of thought processes or noticing that occurred in this task. A third piece of evidence was derived from data obtained from the Moodle website via log files and other resources such as online documents and training quizzes. The purpose was to document which resources the participants accessed relating to data-driven learning training to investigate if those resources aided in their development of corpus competence. The fourth piece of evidence was a quiz developed online to compare the participants with a standard set of items. The quiz was used to investigate which participants successfully or unsuccessfully interpreted the concordances. This instrument, which was analyzed with the Rasch model, allowed for further comparison between the participants’ skill of interpreting concordances. These four data sources were triangulated and in the final analysis cross-referenced to examine how data-driven learning can be successfully applied in a blended online learning environment and how the training of corpus competence aided the learners in interpreting the concordances. / Teaching & Learning

台灣英語教育多以考試取向為主，許多教師進行英語字彙指導時採用填鴨式教學，致使學生無法於新的情境靈活使用字彙。 本研究旨在於探究以語料庫為本之教學對於台灣大學生在英語近似詞學習成效的影響，以台北市某一所大學86位英語學習背景及能力相似之大一生為研究對象。研究人數均分成兩班進行教學實驗，一班為實驗組，以資料觀察法進行教學，另一班為對照組，以傳統形式教學為主，每週一次五十分鐘，共進行十週。資料蒐集包含近似詞學習成就測驗前、後測，並且依據研究對象於實驗教學結束後接受語料觀察教學法回饋問卷，蒐集研究對象對於語料觀察法之反應與感知，進行量化分析。最後，透過訪談高分組和低分組學生，蒐集其質性資料進行研究探討哪些因素會影響不同英語能力學生對於資料觀察法的意願與需求。本研究發現如下: 一、近似詞教學有助於提升台灣大學生的英語字彙能力。兩組教學均在後測有 進步。但就後測成績來說，實驗組顯著優於控制組。資料觀察法之近似詞教學 均較傳統教學法更能有效提升學生的英語字彙能力。 二、在不同程度的學生學習成效上，高、低分組學生均在後測成績有進步。對於 高分組而言，實驗組後測成績顯著優於控制組後測。但對於控制組而言，實驗 組的與控制組的後測成績未呈顯著差異。 三、大部分的學生對於運用資料觀察法學習單字均給予正面回饋，也肯定資料觀 察學習法活動的效益。另外，根據高、低分組學生訪談結果發現，英語程度的 高低的確會影響學生對於資料觀察法的喜愛和需求。高分組的學生希望先以資 料觀察學習法為開端，再以傳統講解式方式做總結。但對低分組的學生而言， 喜歡參與小組討論。由於單字量的不足，低分組學生希望在語料庫為主的教材 旁能附上中文解釋，降低學習焦慮。 根據上述研究結果，本研究建議大學英語教師在教學現場能夠融入語料觀察學 習法並依照不同程度的學生進行教材設計，以助提升學生學習英語單字。 關鍵字:資料觀察學習法、近似詞、語料庫為本 / Corpus Linguistics has progressively become the center in different domains of language research. With such development of large corpora, the potential applications and possibilities of corpora in second language teaching and learning are extended. A discovery-based authentic learning environment is provided as well as created by such corpus-based language learning. Synonym or near-synonym learning is a difficult aspect of vocabulary learning, but a linguistic phenomenon with ubiquity. Hence, this research aims to investigate the effectiveness of the application of data-driven learning (DDL) approach in near-synonyms instruction and compare the teaching effect on the high and low achievers through the near-synonyms instruction. Participants of this study were given instruction throughout the eight-week corpus-based teaching with materials compiled by the teacher. This is a quasi-experimental study consisting of comparison between two experimental conditions, with a pre-post test and control-experimental group design, followed by qualitative method of semi-structure interviews and questionnaire provided to the experimental group of EFL university students in Taiwan. Two intact classes of 86 college students participated in this study. The quantitative analysis of the pre- and posttest scores and questionnaire were conducted through descriptive statistics and frequency analysis in order to explain the learning effects and learners’ perceptions. The results of the study revealed that: (1) participants in the experimental group made significant improvement in the posttest; (2) EFL high proficiency learners with DDL approach performed better than high achievers who were taught by the traditional method. However, low achievers may not be able to benefit from DDL approach in the form of concordance teaching materials; (3) the majority of the participants had positive feedback on DDL activities. Also, types of preferred DDL activities were strongly influenced by students’ proficiency level. Low achievers preferred activities that should involve Chinese translation as the supplementary note while as for the high achievers, they were looking for the teacher’s explanation of words’ usages and functions in the end. This study demonstrates the importance in illuminating the dynamic relationship between DDL approach and second language near-synonyms learning, as well as provides English EFL teachers with a better concept to incorporate corpus or concordance lines into vocabulary instruction. Key words: data-driven Learning, near-synonym, corpus-based approach

Orientador: Paula Tavares Pinto / Resumo: A relação da Linguística de Corpus com o Ensino de Línguas, apesar de receber foco mesmo antes do advento dos computadores, se intensificou por volta da década de 90, momento em que pesquisas em corpora de aprendizes e em Aprendizagem Direcionada por Dados foram enfatizadas. Considerado esse estreitamento, esta pesquisa objetiva compilar quatro corpora de aprendizes a partir do uso autêntico da língua com o intuito de desenvolver atividades didáticas direcionadas por dados dos próprios alunos que promovam nos discentes um perfil autônomo de investigação linguística (mais precisamente das preposições with, in, on, at, for e to). No tocante à fundamentação teórica, destacam-se Prabhu (1987), Skehan (1996), Willis (1996), Nunan (2004) e Ellis (2006) a respeito do Ensino de Línguas por Tarefas, Jenkins (2012) e Neves (2014) que discorrem sobre as ficções de fã. Já sobre a Linguística de Corpus, tem-se Sinclair (1991), Berber Sardinha (2000) e Viana (2011). Granger (1998, 2002, 2013) mais relacionado a Corpus de Aprendizes, e Johns (1991, 1994), Berber Sardinha (2011) e Boulton (2010) no que diz respeito à Aprendizagem Direcionada por Dados. Como metodologia, levantaram-se textos escritos pelos alunos a partir de uma tarefa de produção escrita em que eles redigiram uma ficção de fã. Em seguida, esses textos formaram dois corpora de aprendizes iniciais, que foram analisados com o auxílio da ferramenta AntConc (ANTHONY, 2018) no intuito de observar a presença ou não de inadequações ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Although the relation between Corpus Linguistics and Language Teaching has been emphasized even before the advent of computers, it has been highlighted around the 90s. This was the moment when research on learner corpora and Data-Driven Learning was focused. Having said that, this study aimed to compile four learner corpora based on the authentic use of the language. This was done in order to develop data-driven teaching activities that could promote, among the students, an autonomous profile of linguistic investigation (more precisely about the prepositions with, in, on, at, for and to). Concerning the existing literature, we highlight the works of Prabhu (1987), Skehan (1996), Willis (1996), Nunan (2004) and Ellis (2006) about Task-Based Language Teaching, and Jenkins (2012) and Neves (2014) about fanfictions. In relation to Corpus Linguistics, this study is based on Sinclair (1991), Berber Sardinha (2000) and Viana (2011). Granger (1998, 2012, 2013) is referenced to define learner corpora, and Johns (1991, 1994), Berber Sardinha (2011) and Boulton (2010) to discuss Data-Driven Learning. The methodological approach involved the collection of the compositions from Language Teaching undergraduate students who developed a writing task in which they had to write a fanfiction. These texts composed two learner corpora, which were analyzed with the AntConc tool (ANTHONY, 2018) with the purpose of observing the occurrence of prepositions in English and whether they were accurately ... (Complete abstract click electronic access below) / Mestre