Irregular Verb Acquisition Among EFL and ESL Learners in Cameroon: Peculiarities of Group 4 and 5 Verbs
Bibliographic record
Abstract
Acquisition patterns of irregular verbs by Second and Foreign Language learners of English in Cameroon present remarkable peculiarities that emanate from the country’s unique linguistic identity, where French, English and a multiplicity of local languages are spoken. Following on a previous study (see Ngasu Betek 2020) this study documents these peculiarities, but with a focus on Group 4 and 5 irregular verbs, to ascertain the mastery of English irregular verbs. From a population of nine hundred (900) students from six selected schools in Cameroon, samples of their production in English irregular verb usage were collected. The specific classes were Forms One, Three, Five, (of the Anglophone sub-system of secondary education), and Sixième, Quatrième and Seconde students (from the Francophone sub-system of secondary education). Using Simple Random Sampling Technique, completion tasks were administered and the results analysed to identify and map traceable frequency patterns of use by students. A comparative analysis of the two leaner clusters was carried out which revealed ESL and EFL learners of English in Cameroon both exhibited challenges in using irregular verbs either through overgeneralizations or through morphological distortions of irregular verbs especially at the past tense. This paper thus informs on best practices for successful learning, specifically in the acquisition of irregular verbs by L1 and L2 learners in schools in Cameroon.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".