MétaCan
Menu
Back to cohort

Motivational Strategies

2018· other· en· W4233556390 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe TESOL Encyclopedia of English Language Teaching · 2018
Typeother
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSelf-determination theoryExploitAutonomyPsychologyResource (disambiguation)Context (archaeology)Punishment (psychology)Action (physics)Order (exchange)Intrinsic motivationKnowledge managementSocial psychologyComputer sciencePolitical scienceBusiness

Abstract

fetched live from OpenAlex

Although a comprehensive theory of learner motivation is still lacking, its predominant features are widely agreed upon and can be translated into action in a motivational pedagogy. To generate and sustain learner motivation, extensive taxonomies of motivational strategies, which are highly context‐specific, are accessible to language practitioners. Learner motivation can be influenced by practitioners' strategies, but it is a complex and fluctuating characteristic that cannot be tackled by a traditional linear approach of reward and punishment. An overarching approach in this regard is motivation‐sensitive teaching (MST), which is grounded in self as a motivational resource. It can be actualized through a framework of interlinked macrostrategies that are propped up with applicable microstrategies. Teacher‐afforded motivational strategies promote self‐regulation and autonomy in order to assist learners to exploit self as motivational resource.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.436
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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