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van Lieshout, Catharina; Cardoso, Walcir – Language Learning & Technology, 2022
This study examined the pedagogical use of Google Translate (GT) and its associated text-to-speech synthesis (TTS) and automatic speech recognition (ASR) as tools to assist in the learning of second/foreign language Dutch vocabulary and pronunciation in an autonomous, self-directed learning setting. Thirty participants used GT (its translation,…
Descriptors: Translation, Computational Linguistics, Independent Study, Vocabulary Skills
Godwin-Jones, Robert – Language Learning & Technology, 2022
In recent years, advances in artificial intelligence (AI) have led to significantly improved, or in some cases, completely new digital tools for writing. Systems for writing assessment and assistance based on automated writing evaluation (AWE) have been available for some time. That is the case for machine translation as well. More recent are…
Descriptors: Writing Instruction, Artificial Intelligence, Feedback (Response), Writing Evaluation
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
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
Godwin-Jones, Robert – Language Learning & Technology, 2021
Data collection and analysis is nothing new in computer-assisted language learning, but with the phenomenon of massive sets of human language collected into corpora, and especially integrated into systems driven by artificial intelligence, new opportunities have arisen for language teaching and learning. We are now seeing powerful artificial…
Descriptors: Data Collection, Academic Achievement, Learning Analytics, Computer Assisted Instruction
González-Lloret, Marta – Language Learning & Technology, 2021
In order to develop pragmatic competence in a language other than our own (L2), it is important to have enough knowledge of the cultural norms of the target language and enough opportunities to interact with a wide range of speakers to deploy different speech acts, registers, levels of politeness, conversational moves, and the like. The…
Descriptors: Pragmatics, Second Language Learning, Second Language Instruction, Computer Assisted Instruction
Green, Clarence – Language Learning & Technology, 2022
This paper computes estimates of the potential for Extensive Reading (ER) and Extensive Viewing (EV) to support the academic and discipline-specific vocabulary needs of students. While research into ER/EV for general vocabulary is well-established, only recently has academic vocabulary begun to be researched. Given curriculum time constraints,…
Descriptors: Linguistic Input, Vocabulary Development, Academic Language, Incidental Learning
Wu, Yi-ju – Language Learning & Technology, 2021
Adopting the approaches of "pattern hunting" and "pattern refining" (Kennedy & Miceli, 2001, 2010, 2017), this study investigates how seven freshman English students from Taiwan used the Corpus of Contemporary American English to discover collocation patterns for 30 near-synonymous change-of-state verbs and new ideas about…
Descriptors: Phrase Structure, Teaching Methods, Second Language Learning, Second Language Instruction
Shi, Zhan; Liu, Fengkai; Lai, Chun; Jin, Tan – Language Learning & Technology, 2022
Automated Writing Evaluation (AWE) systems have been found to enhance the accuracy, readability, and cohesion of writing responses (Stevenson & Phakiti, 2019). Previous research indicates that individual learners may have difficulty utilizing content-based AWE feedback and collaborative processing of feedback might help to cope with this…
Descriptors: Writing Instruction, Writing Evaluation, Feedback (Response), Accuracy
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
Lee, Jang Ho; Lee, Hansol; Sert, Cetin – Language Learning & Technology, 2015
The present article deals with the issue of how to create and operate a customizable on-line concordancer from viewpoints of language teachers and with their own laptops. It aims to introduce how to use and manage this application without relying on computer engineers for various pedagogical purposes, focusing on the four beneficial dimensions of…
Descriptors: Indexes, Second Language Learning, Second Language Instruction, Computational Linguistics
Yim, Soobin; Warschauer, Mark – Language Learning & Technology, 2017
The increasingly widespread use of social software (e.g., Wikis, Google Docs) in second language (L2) settings has brought a renewed attention to collaborative writing. Although the current methodological approaches to examining collaborative writing are valuable to understand L2 students' interactional patterns or perceived experiences, they can…
Descriptors: Collaborative Writing, Second Language Learning, Second Language Instruction, Writing Processes
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
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
Godwin-Jones, Robert – Language Learning & Technology, 2017
Although data collection has been used in language learning settings for some time, it is only in recent decades that large corpora have become available, along with efficient tools for their use. Advances in natural language processing (NLP) have enabled rich tagging and annotation of corpus data, essential for their effective use in language…
Descriptors: Computational Linguistics, Second Language Learning, Second Language Instruction, Phrase Structure
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