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Record W7017226634

ACT (like you know what you're doing) Fostering Engagement and Motivation in Language Learners through Drama-Based Pedagogy

2022· other· en· W7017226634 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsComprehension approachDialogicLanguage educationCommunicative competenceLearner autonomyCompetence (human resources)Communicative language teachingSecond-language acquisitionLanguage acquisition
DOInot available

Abstract

fetched live from OpenAlex

This guide considers the complementary nature of a Communicative Language Approach (CLT) and Drama-based Instruction (DBI), within the framework of Self-Determination Theory (SDT), for meaningful and effective learning within the context of French Immersion, Core French, and supporting English language learners when English is the language of instruction. Delivered in six parts, ACT (like you know what you’re doing) serves as a practical guide for language instructors to explicitly connect theory to practice in second language learning (L2) environments. Integrating CLT and DBI nurtures what we inherently do as learners: imagine, communicate, collaborate, and create meaning. \nPart 1 includes a brief overview of second language teaching approaches for language learning, language acquisition (Krashen, 1982), and the more recent extensions that explore “communic-action” (Bourguignon, 2006), that value both the dialogic nature and physicality of language proficiency. Drama-based instruction provides learners with an open space for decoding language and incorporating affective and cognitive skills while problem-solving. Drama as a process, a craft, and a product fosters authentic situations and contexts, bridging what is learned in the classroom to what is needed in the real world. Participation and realization are fundamental modes of engaging with the world (Fettes, 2013). This involves activating imagination and engendering understanding. When a learner is actively engaged in the experience, meaning and understanding are enhanced. The degree to which a learner’s sense of relatedness, autonomy, and competence is nurtured may impact performance, motivation, and well-being (Deci & Ryan, 2012). \nThe empirical studies, found in Part 2, report positive outcomes for student motivation and engagement in second language environments. These serve to example and support a shift to learner-centered instruction, process-oriented instruction, and collaborative learning that embraces diversity as a resource. The proposed pathway to achieving this shift is ACT (like you know what you’re doing), a guide for practicing teachers. The guide (Part 3-6 inclusive) provides \neducators with a selection of scaffolded activities that support concentration, focus, collaboration, physical communication, and vocal communication. In a safe and inclusive learning environment, teachers and students explore the tools of effective communication while integrating expectations of the Ontario Ministry documents, through DBI. Assessment is linked to learning outcomes for elementary language acquisition as outlined in the Common European Framework of Reference (CEFR), Ministry Curriculum documents, and Steps to English Proficiency (STEP). Language learning skills, as outlined in the Strategies Inventory for Language Learners (Oxford, 1990), are explored and practiced. Student autonomy, competence and engagement might be observed through self-assessment and emerging self-efficacy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.017
GPT teacher head0.234
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2022
Admission routes1
Has abstractyes

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