Spanish Interference in EFL Writing Skills: A Case of Ecuadorian Senior High Schools
Bibliographic record
Abstract
Extensive studies have been conducted regarding mother tongue (L1) interference and developing English writing skills. This study, however, aims to investigate the influence of the Spanish language on second language (L2) writing skills at several Ecuadorian senior high schools in Loja. To achieve this, 351 students and 42 teachers from second year senior high schools (public and private) were asked to participate in this study. The instruments for data collection were student and teacher questionnaires, as well as a written test in which students were asked to write a narrative passage. The information gathered from the instruments was then organized and tabulated to determine the various interference variables. Afterwards, the most representative samples from the narrative texts were analyzed based on their semantic, morphological and syntactical features. The results from this study indicate that English grammar and vocabulary were the linguistic areas that suffered the highest level of L1 language interference. The most common Spanish interference errors were misuse of verbs, omission of personal and object pronouns, misuse of prepositions, overuse of articles, and inappropriate/ unnatural word order. Finally, some suggestions are given to teachers in order to help students prevent further Spanish interference problems during writing/composition classes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".