Analysis of Subject-Verb Agreement Errors in Third Person Singular Verb Forms by Spanish University Students: A Corpus-Based Study
Bibliographic record
Abstract
This study is aimed at analyzing subject-verb agreement (SVA) errors with third person singular lexical verbs in the Present Simple by Spanish higher-education students in a computerized learner corpus from Universidad Internacional de La Rioja (UNIR). The corpus is composed of 155 participants and 246 writing samples and it consists of the students’ spontaneous writings in response to a compulsory online forum from the nonlinguistic subject, ICT Tools Applied to the Learning of English, which is included in the curriculum of the Degree in Early Years Education. The SVA errors found in the corpus were classified according to Dulay, Burt and Krashen’s (1982) Surface Strategy Taxonomy, which groups language errors into four different types: omission, addition, misformation and misordering. The results show that the most frequent type of error made by the students is misformation, followed by misordering and by addition, which account for almost 95% of the total number of errors, whereas omission is the least frequent type of error, accounting for only 5% of all the errors. At the same time, the analysis indicates that the errors produced by the students are mainly intralingual, reflecting an inadequate or incomplete learning of the target language, and also interlingual since some errors committed by the learners are related to native language (NL) transfer. These results suggest some pedagogical implications for the teaching and learning of SVA rules which are also included in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".