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Record W4385816208 · doi:10.37213/cjal.2023.32147

Review of Mackey, A. (2020). Interaction, feedback and task research in second language learning: Methods and design. Cambridge University Press.

2023· article· en· W4385816208 on OpenAlexvenueno aff
Ali Shehadeh

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Language acquisitionComputer scienceMedia studiesMathematics educationSociologyLibrary sciencePsychologyLinguisticsEngineeringPhilosophySystems engineering

Abstract

fetched live from OpenAlex

This book by Alison Mackey focuses on three key and essential constructs in the study and research of second/foreign language (L2) learning and teaching.These are Interaction, Feedback, and Task (IFT) studies.This is the first volume and serious attempt to bring these three central constructs in L2 learning and teaching together, explain them, and show how they relate to each other and how they relate to L2 research and study.The book addresses all key topics and developments related to these three areas in an approachable manner from their early inception in the 1970s and 80s till the present time including the use of state-of-the-art technology like eye-tracking, imaging, and fMRI in research into IFT and L2 learning.Using examples from published IFT studies in leading journals, volumes, or dissertations, the book presents clear and practical advice on how to carry out research in these areas, providing step-by-step guides to design and methodological principles.The book consists of a preface, ten chapters, a glossary, a list of references and an index.The preface introduces the book, its goal, significance, and contribution to the body of literature on interaction, feedback and task (IFT) studies.The author states, "Overall, my hope is that this book will support and inspire more research into the three closely related areas of interaction, feedback, and tasks, and how they combine to promote second language learning" (p.xv).Chapter 1, Theory and approaches in research into IFT in L2 learning, introduces the theoretical and empirical foundations of research into IFT and how these three constructs are related to each other and to the wider field of L2 learning and teaching.The chapter discusses open questions and various research problems of relevance to IFT studies.Chapter 2, Designing studies of the roles of IFT in L2 learning, describes the different kinds of research designs and approaches that are available on IFT studies and how these are considered to promote L2 learning.The chapter provides a starting point for people interested in carrying out studies on these topics, or who want to appraise, critique, or better understand research methods used in IFT studies.Chapter 3, Investigating individual differences in IFT studies on aptitude, working memory, and cognitive creativity in L2 learning, describes the measures used in investigating individual differences in IFT studies like working memory and aptitude scores.The chapter also includes a thorough discussion of a relatively under-studied area, cognitive creativity in second language acquisition (SLA), and how this informs research into IFT studies.Chapter 4, Collecting introspective data in IFT research, discusses introspective research methods and how they enhance our understanding of the cognitive and social processes that underlie interactiondriven learning.In the author's words, the chapter describes "a range of commonly used tools for obtaining introspections, including stimulated recalls, think-alouds, interviews, discourse completion tasks, and self-reports on social media, all in the context of research on interaction, feedback, and tasks" (p.71).Chapter 5, Creating and using surveys, interviews, and mixed methods for research into IFT and L2 learning, focuses on surveybased research like interviews.The chapter discusses issues like designing questionnaires, question types, and how we develop and administer surveys.The chapter also explains the

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.332
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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