A Corpus-Based Study on the Use of Past Tense Auxiliary ‘Be’ in Argumentative Essays of Malaysian ESL Learners
Bibliographic record
Abstract
This research is a corpus-based study of secondary and college ESL Malaysian learner’s written work by identifying and classifying the types of errors in the Past Tense Auxiliary ‘Be’. This This research studied the past tense auxiliary ‘be’, types of past tense auxiliary ‘be’ errors and frequency of past tense auxiliary ‘be’ errors found in the Malaysian Corpus of Students’ Argumentative Writing (MCSAW) corpus using the WordSmith Tools Version 4.0 and using the Error Analysis (EA) approach. The findings revealed that there are seven types of errors. They are Tense Shift, Agreement, Missing Auxiliary Be, Wrong Verb Form, Addition and Misformation and Misordering. This study can be used as a guide for English Language teachers to identify the most common errors in using the Past Tense Auxiliary ‘Be’ made by the ESL learners and decide what remedial action can be taken to prevent them from making these errors. It can also help teachers improvise and develop materials which are not only more suitable but also cater to the needs of the students. In addition to the materials, teachers can also revise their teaching approaches and strategies to ensure effective teaching and learning of these grammar components.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".