Does the Quality of Source Notes Matter? An Exploratory Study of Source-based Academic Writing
Bibliographic record
Abstract
Integrated writing tasks have increased in popularity in second language writing classrooms. Extensive research on these tasks has examined the challenges that students face when completing such tasks. One significant challenge is the transformation of source language use when students integrate source information into their own essays. However, little is known about the relationship between students’ source notes and the quality of the essays that they produce. This exploratory study examined this issue by investigating the relationship between characteristics of English for academic purposes (EAP) students’ notes (N = 24) and their essay scores. The students’ notes were coded in terms of how they appropriated information from the source texts using four categories: copied, copied with changes, copied with gaps, and paraphrased. A multiple linear regression revealed that essay scores were predicted by the degree to which the students transformed source language and avoided copying. The implications of these findings for second language (L2) writing pedagogy and assessment are discussed. Les tâches de rédaction intégrée deviennent de plus en plus populaires dans les classes de rédaction en seconde langue. Ces tâches ont fait l’objet de nombreuses recherches qui ont étudié les défis auxquels font face les étudiants lorsqu’ils les exécutent. Un défi de taille est la transformation de l’utilisation de la langue source lorsque les étudiants intègrent l’information tirée des sources dans leurs rédactions. Cependant, on ne sait pas grand chose sur la relation entre les notes des étudiants provenant des sources et la qualité des rédactions qu’ils produisent. Cette étude exploratoire s’est intéressée à ce problème étudiant la relation entre les caractéris- tiques des notes des étudiants dans les cours d’anglais académique (N = 24) et les notes obtenues pour leurs rédactions. Les notes des étudiants ont été classées en quatre catégories selon la façon dont ils s’appropriaient l’information des textes sources : copiées, copiées avec des changements, copiées avec des lacunes et para- phrasées. Une régression multiple linéaire a révélé que les notes obtenues pour les rédactions étaient prédites par le degré auquel les étudiants avaient transformé la langue source et avaient évité de copier. On discute des implications de ces résultats pour la pédagogie et l’évaluation de la rédaction en langue seconde (L 2).
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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 | 0.017 | 0.133 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| 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".