Bibliographic record
Abstract
Academic writing in higher education has been a long-standing priority, with a greater need for writing supports noted in the past decades (Wingate & Tribble, 2012) and an increasing focus on discipline-specific language in order for students to learn to write and communicate effectively as professionals in their chosen fields (Grzyb et al., 2018). This study examined student learning outcomesin two writingintensive designated health professions courses (Nursing and Public Health). Students completed assignments throughout the semester. One course section required students to turn in a final paper without receiving feedback during the writing process while, in the other course, students received feedback on sections of the final paper throughout the semester. At the final exam stage, students were asked to reflect on their learning experience in the course. At the end of the semester, students submitted their final paper and completed a learning reflection to meet the course requirements. To inform a course revision, student paper and learning reflection narratives were analyzed. Narratives were de-identified and inductively coded by a single coder. First-round coding employed descriptive and in vivo coding to explore the data. The codebook for second-round coding was refined and codes were classified within the headings descriptive, emotion, and value. Findings indicate that students felt they had increased capacity for reflection when feedback was provided throughout the semester. They also felt they benefited from integrating feedback on the credibility of sources, organization, and citations. Integrating feedback and reflection opportunities contributed to greater student learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".