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Emily A. Hellmich; Kimberly Vinall – Language Learning & Technology, 2023
The use of machine translation (MT) tools remains controversial among language instructors, with limited integration into classroom practices. While much of the existing research into MT and language education has explored instructor perceptions, less is known about how students actually use MT or how student use compares to instructor beliefs and…
Descriptors: Translation, Second Language Learning, Second Language Instruction, Computational Linguistics
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Dongkawang Shin; Yuah V. Chon – Language Learning & Technology, 2023
Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57…
Descriptors: Second Language Learning, Second Language Instruction, Translation, Computer Software
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Zhang, Hong; Torres-Hostench, Olga – Language Learning & Technology, 2022
The main purpose of this study is to evaluate the effectiveness of Machine Translation Post-Editing (MTPE) training for FL students. Our hypothesis was that with specific MTPE training, students will able to detect and correct machine translation mistakes in their FL. Training materials were developed to detect six typical mistakes from Machine…
Descriptors: Computational Linguistics, Translation, Second Language Learning, Second Language Instruction
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Matthews, Joshua; Wijeyewardene, Ingrid – Language Learning & Technology, 2018
Despite the current potential to use computers to automatically generate a large range of text-based indices, many issues remain unresolved about how to apply these data in established language teaching and assessment contexts. One way to resolve these issues is to explore the degree to which automatically generated indices, which are reflective…
Descriptors: Correlation, Robotics, Second Language Learning, Second Language Instruction
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Lay, Keith J.; Yavuz, Mehmet A. – Language Learning & Technology, 2020
This study investigates the possibility and efficacy of paper-based, in-class, data-driven learning (DDL) of academic lexical bundles below the C1 level of proficiency described by the Common European Framework of Reference (CEFR; advanced high ACTFL). A two-stage experimental design involving three groups (n = 41) and 24 two-to-four word academic…
Descriptors: Language Proficiency, Rating Scales, Guidelines, Second Language Learning
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Ackerley, Katherine – Language Learning & Technology, 2017
This study analyses the effects of data-driven learning (DDL) on the phraseology used by 223 English students at an Italian university. The students studied the genre of opinion survey reports through paper-based and hands-on exploration of a reference corpus. They then wrote their own report and a learner corpus of these texts was compiled. A…
Descriptors: Phrase Structure, Computational Linguistics, English (Second Language), Second Language Learning
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Li, Shuangling – Language Learning & Technology, 2017
This article investigates the role of direct corpus use in learners' collocational competence in academic writing. An experiment was conducted between two groups of Chinese postgraduates who had no previous knowledge of corpora. It was embedded in a regular 4-month linguistics course in the students' programmes, where a corpus-assisted method was…
Descriptors: Phrase Structure, Computational Linguistics, Accuracy, Graduate Students
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Elgort, Irina – Language Learning & Technology, 2017
This study investigates differences in the language and discourse characteristics of course blogs and traditional academic submissions produced in English by native (L1) and advanced second language (L2) writers. One hundred and fifty-two texts generated by 38 graduate students within the context of the same Master's level course were analysed…
Descriptors: Graduate Students, Student Journals, Electronic Journals, Writing Assignments
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Bardovi-Harlig, Kathleen; Mossman, Sabrina; Su, Yunwen – Language Learning & Technology, 2017
This study compares the effect of using corpus-based materials and activities for the instruction of pragmatic routines under two conditions: implementing direct corpus searches by learners during classroom instruction and working with teacher-developed corpus-based materials. The outcome is compared to a repeated-test control group. Pragmatic…
Descriptors: Computational Linguistics, Pragmatics, Control Groups, Comparative Analysis
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Cotos, Elena; Link, Stephanie; Huffman, Sarah – Language Learning & Technology, 2017
To better understand the promising effects of data-driven learning (DDL) on language learning processes and outcomes, this study explored DDL learning events enabled by the Research Writing Tutor (RWT), a web-based platform containing an English language corpus annotated to enhance rhetorical input, a concordancer that was searchable for…
Descriptors: Data, Computer Assisted Instruction, Mixed Methods Research, Graduate Students
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MacDonald, Penny; Garcia-Carbonell, Amparo; Carot, Sierra, Jose Miguel – Language Learning & Technology, 2013
This study focuses on the computer-aided analysis of interlanguage errors made by the participants in the telematic simulation IDEELS (Intercultural Dynamics in European Education through on-Line Simulation). The synchronous and asynchronous communication analysed was part of the MiLC Corpus, a multilingual learner corpus of texts written by…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Interlanguage
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Thompson, Paul; Tribble, Chris – Language Learning & Technology, 2001
Presents a classification scheme and the results of applying this scheme to the coding of academic texts in a corpus. The texts are doctoral theses from agricultural botany and agricultural economics departments. Results lead to a comparison of the citation practices of writers in different disciplines and the different rhetorical practices of…
Descriptors: Agriculture, Botany, Citations (References), Comparative Analysis