The Effect of Spatial Intelligence-based Metalinguistic Written Corrective Feedback on EFL Learners’ Development in Writing
Bibliographic record
Abstract
Correcting and providing feedback to the written work of the learners has always been one of the hotly-debatedissues over the last decades. While a group of scholars argue in favor of the effectiveness of the written corrective(CF) feedback, others question the utility and usefulness of the CF on writing of the learners. Even there seem to befewer consensuses on the typology of the CF. Metalinguistic written corrective feedback (CF) (e.g., briefgrammatical descriptions and error codes) is a type of written feedback, through which teacher gives metalinguisticclue to the nature of the errors (Ellis, 2009). In this study, a different type of metalinguistic feedback, conceptualizedas spatial intelligence-based (SIB) metalinguistic written CF_ using the colorful stationery to write, highlight, locate,or underline the linguistic errors of the learners while giving feedback_ was used while providing feedback to thelearner’s work. In order to investigate the effectiveness of SIB metalinguistic written CF on English as a foreignlanguage (EFL) learners’ development in writing, 47 intermediate learners were randomly assigned into two groups.The learners in the first group received SIB metalinguistic written CF for their errors in writing, while the ones in thesecond group only obtained metalinguistic written CF for their errors. An independent samples t-test applied on thescores achieved from a posttest showed a significant difference in scores of the first group and that of theexperimental group. Results indicated that the accuracy (mechanics) and style of the writing of the first group ofstudents who received SIB correction for their linguistic errors exceled that of the second group students whosereceived written correction was only metalinguistic. However, there was no significant difference between thegroups in the content, and organization of their writing.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".