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Record Nr. |
UNINA9910459726703321 |
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Titolo |
Learner corpora in language testing and assessment / / edited by Marcus Callies, University of Bremen, Sandra Götz, Justus Liebig University, Giessen |
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Pubbl/distr/stampa |
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Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2015 |
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©2015 |
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ISBN |
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Descrizione fisica |
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1 online resource (226 p.) |
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Collana |
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Studies in Corpus Linguistics (SCL), , 1388-0373 ; ; Volume 70 |
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Disciplina |
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Soggetti |
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Language and languages - Ability testing - Data processing |
Language and languages - Study and teaching - Data processing |
Electronic data processing |
Information storage and retrieval systems |
Punched card systems |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Learner Corpora in Language Testing and Assessment; Editorial page; Title page; LCC data; Table of contents; Learner corpora in language testing and assessment: Prospects and challenges; Acknowledgements; References; Section I. New corpus resources, tools and methods; The Marburg Corpus of Intermediate Learner English (MILE); 1. Introduction; 2. Learner corpora in the light of the CEFR; 2.1 The raw data; 2.2 The annotation; 3. MILE - design and compilation; 4. Conclusion; References; Avalingua: Natural language processing for automatic error detection; 1. Introduction |
2. Automatic error detection and correction2.1 Previous research; 2.2 Applications; 3. Avalingua; 3.1 Target; 3.2 Motivations; 3.3 The system; 3.3.1 Lexical module; 3.3.2 Spelling module; 3.3.3 Syntactic module; 3.3.4 Language identification; 3.3.5 Student model; 4. System evaluation; 4.1 A specific implementation; 4.2 The learner corpora; 4.3 |
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Evaluation protocol; 4.4 Results; 4.5 Error analysis and discussion; 5. Conclusions; References; Data commentary in science writing: Using a small, specialized corpus for formative assessment practices; 1. Background and aims |
2. Approaching data commentary from a pedagogical perspective: The case for small, specialized corpora annotated for discourse movesin the ESP classroom3. A small, specialized corpus of data commentaries; 4. The discourse annotation model; 5. Self-assessment and the role of the corpus; 5.1 Towards corpus-informed formative self-assessment activities; 5.1.1 Teacher-designed activities on moves in data commentaries; 5.1.2 Teacher-designed peer-assessment activities of master's thesis corpus data; 5.1.3 Teacher- and student-initiated activities involving students' own writing |
6. Final remarks and outlookAcknowledgement; References; First steps in assigning proficiency to texts in a learner corpus of computer-mediated communication; 1. Introduction; 2. The CMC Learner Corpus; 2.1 CMC in the classroom; 2.2 The CMC corpora; 3. Criteria for assigning proficiency; 3.1 Following established practice; 3.2 Practicality and ease of implementation; 3.3 Reference native-speaker norms; 4. Method; 4.1 Performance decision trees; 4.2 Sequence of PDTs; 4.3 PDT for accuracy; 4.4 PDT for fluency; 4.5 PDT for complexity; 5. Results; 5.1 Preliminary results of proficiency ratings |
5.2 Descriptive statistics5.3 Vocabulary level; 6. Discussion; 6.1 Validity of the proficiency measurement tool; 6.2 PDT proficiency levels and institutional status; 6.3 PDT proficiency levels and individual variation; 6.4 Limitations of the proposed measurement tool; 7. Conclusion; References; Appendix; Section II. Data-driven approaches to the assessment of proficiency; The English Vocabulary Profile as a benchmark for assigning levels to learner corpus data ; 1. Introduction; 2. Developmental indices and language proficiency; 3. The CEFR and reference level descriptions |
4. The English Profile and criterial features |
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