<scp> <b>CREATING WELCOME LEARNING</b> </scp> <b> <scp>ENVIRONMENTS</scp> : <scp>USING</scp> </b> <scp> <b>CREATIVE ARTS METHODS IN LANGUAGE CLASSROOMS</b> </scp> , Edited by JaneAndrews and MaryamAlmohammad, Multilingual Matters. Price <scp>USD</scp> 69.95 (hardcover, <scp>ISBN</scp> : 978–1–78892‐578‐5), <scp>USD</scp> 19.95 (paperback, <scp>ISBN</scp> : 978–1–78892‐580‐8), <scp>USD</scp> 13.00 (ebook, <scp>ISBN</scp> 978–1–78892‐579‐2). 150 pages
Bibliographic record
Abstract
Educators have long integrated the arts into language education, enhancing instruction and benefiting students in analysis, comprehension, and communication (Farokhi & Hashemi, 2012).Increasing research has shown that arts activities positively impact learners' linguistic skills and abilities (Thulasivanthana, 2020).The book under review, edited by Jane Andrews and Maryam Almohammad, adds to the growing scholarship on incorporating arts into language education.It is a collection of chapters written by teachers, creative practitioners, and researchers, focusing on creating inclusive and equitable learning environments for English as an additional language (EAL) learners in the United Kingdom.The settings where these creative activities developed span various educational environments, such as primary schools, secondary schools, a school catering to multilingual speakers with special educational needs and hearing-impaired students.The book aims to present principles that merge creative arts techniques with teaching methods that bolster English language skills in schools or colleges in anglophone societies.These principles are (1) -valuing and obtaining the expertise of all participants through professional discussions; (2) experiential learning-encouraging teachers to engage in hands-on experiences with arts-based approaches and to tailor activities to their own teaching contexts; and (3) decolonizing ethos-avoiding exploitative practices.In the opening chapter, Jane Andrews and Maryam Almohammad review previous research, highlighting the central role of the arts in enhancing identity expression and fostering inclusivity among multilingual learners.As they delve into the implementation of arts-based teaching, the authors candidly mention potential challenges: increased demands on educators, the quest for resources, and the essential need for technological and confidence support.They advocate for collaborative efforts from multiple specialists and the broader community to address these challenges.This collaborative ethos is foundational to the arts-based teaching methodology presented in the book.The chapter concludes with a concise summary of the projects shaping the book and provides an overview of the book's structure.In Chapter 2, Alison Phipps addresses the challenges educational systems face when they emphasize curriculum and English development while sidelining students' own linguistic resources.Such an emphasis can create an unwelcoming environment for international learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.111 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".