Exploring Pre-service Teachers’ Perception of Interactional Activities in Lesson Planning
Bibliographic record
Abstract
With the plethoric number of students in Cameroonian classrooms today, there is a common cry from teachers about the impracticability of creating interactional language lessons in the English as a foreign language (EFL) classroom. The implication of this is that, if interactional lessons have been identified as cardinal in the process of language learning, our learners are not or are less likely to be learning English as a foreign language. This paper holds that no teacher will be able to create interactive lessons if they do not fully understand what it is and how it works. The paper therefore investigates student teachers’ perception of interaction on a less theoretical stance to posit that their readiness to teach is governed by their perception of the concept of interaction and an understanding of how it works. Data were collected from two batches of 30 pre-students students from the Department of English at ENS Yaounde in 2018 and 2019. Upon completion of three semesters that qualify them to do practicum, they were asked to submit language lessons meant for interactive classrooms. The lessons were coded identifying trends related to interactive activities and later classified. Evidence from the data demonstrates significant recourse to previous learning culture of top-bottom lessons and high incidences of question–and-answer as a dominant pattern of interaction. In keeping with this, the paper questions the training approaches and argues that the inability of pre-service teachers to demonstrate practical understanding of classroom interaction is counterproductive to what potentially holds as language learning and that modules of training which allow teachers to seek patterns would be more productive than those which train them on classroom interaction.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".