MétaCan
Menu
Back to cohort
Record W2800825920 · doi:10.3968/10160

Moroccan EFL Secondary School Teachers’ Perceptions and Practices of Learner-Centered Teaching in Taroudant Directorate of Education, Morocco

2018· article· en· W2800825920 on OpenAlexvenueno aff
Abdallah Ghaicha, Karima Mezouari

Bibliographic record

VenueHigher education of social science · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionSchool teachersMathematics educationCurriculumClass (philosophy)PsychologyPedagogyQualitative researchMedical educationSociologyComputer scienceMedicineSocial science

Abstract

fetched live from OpenAlex

The present paper reports on a qualitative study that (a) investigated Moroccan EFL secondary school teachers’ perceptions of LCT, (b) assessed how these perceptions affect their actual teaching practices, and (c) surveyed the different constraints to the implementation of LCT in Moroccan EFL classes. Structured open-ended interviews and non-participant classroom observations were used to collect data from four EFL secondary school teachers belonging to three public secondary schools in the provincial directorate for national education in Taroudant, Morocco. First, teachers were interviewed to obtain information related to their perceptions and understanding of LCT as well as the challenges facing its implementation. Then, they were observed to gain complementary data about teachers’ practices of LCT. The results have revealed that teachers do hold right perceptions and good understanding of LCT. Yet, due to constraints such as the standardized curriculum and examination, lack of materials and large class size, teachers find themselves obliged to keep different traditional practices.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.346
Teacher spread0.313 · 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 designQualitative
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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHigher education of social scienceSame topicSecond Language Learning and TeachingFrench-language works237,207