刊讯｜SSCI 期刊《语言学习与技术》2022年第2期

LANGUAGE LEARNING & TECHNOLOGY

Volume 26, Number 2, June 2022

LANGUAGE LEARING & TECHNOLOGY（SSCI一区，2021 IF：4.694）2022年第2期共发文9篇，其中研究性论文7篇，媒体评论2篇。本期为“自动写作评估”（Automated Writing Evaluation）专刊，研究论文涉及自动化写作评估、基于体裁的教学法、自动反馈、眼动跟踪、自动纠错反馈、机器翻译、人工智能工具、第二语言写作、移动辅助语言学习等。欢迎转发扩散！（2022年已更完）

目录

Articles

■ Genre-based AWE system for engineering graduate writing: Development and evaluation, by Feng Hui-Hsien, Chukharev-Hudilainen Evgeny, Pages 58–77.

■ L2 learners’ engagement with automated feedback: An eye-tracking study, by Liu Sha, Yu Guoxing, Pages 78–105.

■ Enhancing the use of evidence in argumentative writing through collaborative processing of content- based automated writing evaluation feedback, by Shi Zhan, Liu Fengkai, Lai Chun, Jin Tan, Pages 106–128.

■ Exploring AWE-supported writing process: An activity theory perspective, by Zhenzhen Chen, Weichao Chen, Jiyou Jia & Huixiao Le, Pages 129-148.

Columns

■ Partnering with AI: Intelligent writing assistance and instructed language learning, by Robert Godwin-Jones, Pages 5-24.

■ SMART Teacher Lab: A learning platform for the professional development of EFL teachers, by Heyoung Kim & Jang Ho Lee, Pages 25-37.

■ A study of pre-service EFL teachers’ acceptance of online teaching and the influencing factors, by Weifeng Sun & Bin Zou, Pages 38-49.

Media Reviews

■ Review of Researching and teaching second language writing in the digital age, by Cherub, Aubri, Kessler, Matt, Pages 50-53.

■ Review of Language teacher development in digital contexts, by Lynn Nakazawa, Maria Laura Zalazar & Kristin Rock, Pages 435-458.

摘要

Automatic pronunciation assessment vs. automatic speech recognition: A study of conflicting conditions for L2-English

Feng Hui-Hsien, National Kaohsiung University of Science and Technology

Chukharev-Hudilainen Evgeny, lowa State University

Abstract 

Automated writing evaluation (AWE) systems have been introduced to ESL/EFL classes in the hopes of reducing teachers' workloads and improving students' writing by providing instant holistic scores and corrective feedback (Jiang & Yu, 2020; Link et al., 2014; Ranalli & Yamashita, 2019; Warschauer & Ware, 2006). When it comes to genre-specific writing, general AWE feedback may be insufficient because communicative purposes should be achieved, for which feedback is needed beyond grammar and mechanics. However, very few genre-specific AWE systems based on rhetorical move analysis have been developed. Therefore, the present study reports on the development and evaluation of a genre-based AWE system to facilitate Taiwanese engineering graduate students' writing of research abstracts. This AWE system provides automated feedback on two linguistic features, lexical bundles and grammatical categories of verbs (i.e., tense, aspect, and voice), associated with moves in abstracts. The feedback was designed to be co-constructed between learners and computers in order to promote interaction. The effectiveness of the AWE system was evaluated following Chapelle's (2001) computer-assisted language learning evaluation framework. The findings revealed positive effects; with appropriate guidance, the AWE system was able to draw participants' attention to and enhanced their use of these two linguistic features to achieve the communicative purposes of rhetorical moves in their abstracts.

Key words Automated Writing Evaluation, Genre-based Pedagogy, Lexical Bundles, Grammatical Categories of Verbs

L2 learners’ engagement with automated feedback: An eye-tracking study

Liu Sha, University of Bristol

Yu Guoxing, University of Bristol

Abstract 

This study used eye-tracking, in combination with stimulated recalls and reflective journals, to investigate L2 learners’ engagement with automated feedback and the impact of feedback explicitness and accuracy on their engagement. Twenty-four Chinese EFL learners revised their writing through Write & Improvewith Cambridge, a new automated writing evaluation system that generates automated feedback with three different levels of explicitness. Data from multiple perspectives were collected and examined, including participants’ eye movements, their stimulated recalls, and their responses/revisions to automated feedback on their multiple drafts. The results revealed that participants spent significantly more time and expended more cognitive effort in processing indirect than direct feedback. However, a lower percentage of indirect feedback was taken up, and the revisions participants made based on such feedback were less successful. These findings suggest feedback explicitness as a determining factor affecting learners’ engagement with automated feedback and point to the need for timely, supplemental teacher or peer scaffolding in addition to automated feedback. The results also suggest that AWE tools need to be constantly updated to improve their feedback accuracy, as error-prone feedback may cause participants to make inaccurate amendments to their writing. In addition, teachers should help learners confirm the accuracy of AWE feedback

Key words Explicitness of Automated Feedback, Accuracy of Automated Feedback, L2 Learner Engagement, Eye-tracking

Enhancing the use of evidence in argumentative writing through collaborative processing of content- based automated writing evaluation feedback

Shi Zhan, The University of Hong Kong

Liu Fengkai, City University of Hong Kong

Lai Chun, The University of Hong Kong

Jin Tan, South China Normal University

Abstract

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 challenge (Elabdali, 2021; Wang et al., 2020). However, how learners might collaboratively process content-based AWE feedback remains an open question. This study intends to fill this gap by following a group of five Chinese undergraduate EFL students’ collaborative processingof content-based AWE feedback on the use of evidence in L2 argumentative writing during five writing tasks over a semester. Student collaboration was examined through tracking the recordings of collaborative discussion sessions as well as their written drafts and revisions, and through collecting interview responses from individual learners. The findings revealed that the collaborative processing of AWE feedback was experienced in three phases, namely the trustful phase, skeptical phase, and critical phase. Although content-based AWE feedback could facilitate the development of some aspects of evidence use, collaborative discourses were instrumental in developing learners’ understanding and skills for certain aspects of evidence use that AWE feedback failed to address. The findings suggest that collaborative discourse around content-based AWE feedback can be an important pedagogical move in realizing the potential of AWE feedback for writing development.

Key words Content-based AWE Feedback, Collaborative Processing of Feedback, Using Evidence in Argumentative Writing

Exploring AWE-supported writing process: An activity theory perspective

Chen Zhenzhen, Beijing University of Posts and Telecommunications

Chen Weichao, Baylor College of Medicine

Jia Jiyou, Peking University

Le Huixiao, Peking University

Abstract Despite the growing interest in investigating the pedagogical application of Automated Writing Evaluation (AWE) systems, studies on the process of AWE-supported writing are still scant. Adopting activity theory as the framework, this qualitative study aims to examine how students incorporated AWE feedback into their writing in an English as a foreign language setting. We conducted semi-structured interviews with four Chinese students sampled from two classes and collected their AWE submissions and feedback for data analysis. Our findings demonstrate that AWE-supported writing is a tool-mediated, purposive, and collective activity shaped by individual and contextual factors. Students used various strategies to attain their learning goals and to address the tensions arising from their activity systems. This study contributes to the research on the effectiveness of AWE by assuming a process-oriented approach that was informed by activity theory. Our findings also shed light on the complex process of second language writing mediated by new technology innovations. Pedagogical implications of our findings are discussed in the conclusion.

Key words Computer-Assisted Language Learning (CALL), Automated Writing Evaluation (AWE), Activity Theory, L2 Writing

Partnering with AI: Intelligent writing assistance and instructed language learning

Godwin-Jones Robert, Virginia Commonwealth University

Abstract 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 synchronous feedback tools, such as Grammarly. That tool incorporates, as do others, predictive text technology, supplying automated sentence completion. Emerging writing assistance goes further, generating an entire text in response to a brief prompt. That capacity, along with significantly improved performance of both automated feedback systems and machine translation, is enabled through advances in AI, built on ever larger datasets and deep machine learning. While they differ in interface, functionality, and target audience, the available and emerging set of intelligent writing tools can be used to help learners improve the quality of their written texts. However, their use in instructional language learning has in some cases been controversial. In this column, we will be examining AI-enabled writing tools, reviewing the findings from research studies, and discussing their use in instructional settings. When integrated into writing instruction and practice, these digital tools have been found to offer significant benefits to both students and teachers. Teacher mediation aids learners in becoming informed consumers of language technology, as well as helping them to gain meta-linguistic knowledge. For researchers, intelligent writing tool use is optimally analyzed from a broad ecological perspective that examines the dynamic interplay of learner, software, and instructional environment. 

Key words Automatic Corrective Feedback, Machine Translation, AI Tools, Second Language Writing

SMART Teacher Lab: A learning platform for the professional development of EFL teachers

Kim Heyoung, Chung-Ang University

Lee Jang Ho, Chung-Ang University

Abstract This article introduces the structure and content of an online learning platform called SMART Teacher Lab (STL, henceforth) implemented at the authors’ university since 2014. “STL” is an online platform specifically built for the professional development of pre- and in-service English as a Foreign Language (EFL) teachers, containing and accumulating various types of hands-on and field-specific educational resources. These resources include information on the preparation of teaching practicums, video clips of teaching demonstrations, student or teacher interviews, lecture materials on recent educational approaches and technology, and more. STL was originally designed as an open-access mobile-based platform based on the previous literature of non-formal learning, and a development-centered view of bottom-up teacher education. Providing examples of resources related to English education majors and highlighting the strengths of STL, this article aims to emphasize the importance of such a platform for the successful and sustainable professional development of EFL teachers. Suggestions for EFL teacher trainers in other pedagogical contexts are also included.

Key words EFL Teacher Education, Mobile-Assisted Language Learning, Non-Formal Learning, Open Learning Platform

A study of pre-service EFL teachers’ acceptance of online teaching and the influencing factors

Sun Weifeng, Xuzhou Huohua School; Soochow University

Zou Bin, Xi'an Jiaotong-Liverpool University

Abstract It is expected that the field of language education will see an increased need for teachers to accept online teaching. Based on the Technology Acceptance Model, this study examined pre-service EFL teachers’ acceptance of online teaching and the factors influencing them. The participants were TESOL majors at three universities in China. The data were collected from a questionnaire survey with 204 participants andsemi-structured individual interviews with 12 participants. The study reveals that pre-service English teachers generally accept online teaching after completing one-semester of purely online learning during the COVID-19 pandemic. The results also suggest that participants’ enjoyable experiences in using online technologies, perceived usefulness of online teaching, social influences, and technological pedagogical content knowledge influence their acceptance of online teaching. 

Key words Technology Acceptance Model (TAM), EFL, Online Teaching, Pre-service Teachers

Review of Researching and teaching second language writing in the digital age

Cherub Aubri, University of South Florida

Kessler Matt, University of South Florida

Abstract Technology use in the second language (L2) classroom has become an integral fixture, and now more than ever both teachers and their students are expected to possess considerable technological literacy in order to function in a world that is highly digital in nature (Oskoz & Elola, 2020). The research landscape, too, is increasingly reflecting such societal changes, especially in the domain of L2 writing. Within this subdiscipline, topics such as multimodality, computer-mediated collaborative writing, and automated writing evaluation––the topic of this special issue––have grown exponentially in prominence over the past decade. 

Review of Video enhanced observation for language teaching: Reflection and professional development

Pelin Irgin, TED University

Abstract In the unprecedented times of emergency remote teaching caused by the COVID-19 global pandemic, the use of innovative resources became indispensable for teachers (Bao, 2020). Teachers all around the worldhave been faced with the challenge of selecting and implementing digital tools during this global health crisis. To meet remote teaching demands during and post COVID-19, teachers need to be provided withtraining for the use of various technological tools and resources. 

期刊简介

Language Learning & Technology (LLT) is a free, fully-refereed, open journal which has been published exclusively online since July 1997. Originally published tri-annually, the journal is now published continously throughout the year by the National Foreign Language Resource Center at the University of Hawai‘i at Mānoa. The journal seeks to disseminate research to foreign and second language educators on issues related to technology and language education. The focus of LLT is not technology per se, but rather issues related to language learning and language teaching, and how they are affected or enhanced by the use of digital technologies. LLT has an editorial board of scholars in the fields of second language acquisition and computer-assisted language learning.

《语言学习与技术》是一个免费的，全文检索，开放的杂志，已在1997年7月独家在线出版。最初每三年出版一次，现在由位于 Mānoa 的夏威夷大学国家外语资源中心全年连续出版。本刊旨在向外国和第二语言教育者传播有关技术和语言教育问题的研究成果。语言教学的重点不是技术本身，而是与语言学习和语言教学有关的问题，以及它们如何受到数字技术的影响或加强。LLT 拥有第二语言习得和计算机辅助语言学习领域的学者编辑委员会。

官网地址：

https://www.lltjournal.org/

本文来源：LANGUAGE LEANRING & TECHNOLOGY官网

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