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Record W4396612079 · doi:10.5430/jct.v13n2p22

Effective Teaching Strategies for Multilevel Classes: A Focus on Alternating Instructional Modes

2024· article· en· W4396612079 on OpenAlexvenueno aff
Fatima Zahra El Ouahabi, Rachid Drissi El Bouzaidi, Abdellah Chaiba, Lahbib Hamdaoui, Mohamed Erragragui

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsFocus (optics)Computer scienceMathematics educationPsychologyPhysicsOptics

Abstract

fetched live from OpenAlex

This paper explores a teaching strategy in multilevel classes (MLCs) to tackle learning time loss and teacher strain. Focused on Moroccan primary education, the strategy involves alternating traditional and virtual teaching methods. Expanding previous research, this work 3rd year primary Arabic language and 4th year primary Islamic Education double-level classing. Using NetSupport School software, lessons were conducted with real and virtual teachers’ presences, analyzing impacts on lesson duration, teacher effort, and learners’ time on task. Results demonstrate the strategy significantly increased effective lesson duration in experimental groups, reducing teacher intervention time and increasing learner task duration compared to controls. Virtual teacher presence showed promise in managing time-out and enhancing student engagement, particularly with the learners' permanent teacher. Challenges emerged when learners interacted with alternative virtual teachers, highlighting the significance of the teacher's perceived status on student engagement and non-adaptation of the learners to a new virtual teacher. For a deep interpretation of this non-adaptation, the importance of the perceived "status" of the teacher has been developed. This study underscores strategic teaching's importance in MLCs to optimize learning outcomes and mitigate time constraints. It emphasizes the pivotal role of the teacher's perceived status in student engagement dynamics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.027
GPT teacher head0.378
Teacher spread0.351 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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