The Impact of the Data-Driven Learning Approach on ESL Writers’ Citation Patterns
Bibliographic record
Abstract
This study reports the impact of the data-driven learning (DDL) approach on ESL Saudi writers’ general citation patterns that contribute to their general authorial voice. Specifically, the study examines the effects of the DDL activities on ESL writers’ use of integral and non-integral citation patterns based on Swales’ (1981, 1986, and 1990) modal of citation analysis and the extended scheme of classification set by Thompson & Tribble (2001). Guided use of both the Michigan Corpus of Upper-Level Student Papers (MICUSP) and WordandPhrase.info has been designed, implemented, and assessed with a representative sample of 32 ESL upper-intermediate and advanced writers in the Department of Translation in College of Languages at Princess Nourah bint Abul Rahman University (PNU). The effectiveness of the DDL activities in improving the writers’ use of the citation patterns in composition of assignments is measured via a repeated measure paired t test. The study evaluates writers' authorial voice in terms of their use of integral and non-integral citation patterns. The quantitative analysis reveals that participants’ integral patterns (n = 398) of citation significantly outnumbered non-integral patterns (n = 126). The verb-controlling pattern occurred the most (n = 320), constituting 61% of total citation patterns. Results of the paired sample t test reveals a significant statistical difference between participants’ performances before and after the integration of the DDL activities, with the mean value being increased from 2.285 to 3.778. These results inform pedagogical implications of the DDL approach in ESL writing. The conceptual framework implementing the DDL approach in the present study provides guidance for applying corpus-informed tools when designing writing activities for upper-intermediate to advanced ESL learners.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".