Structured instructional design for integrated language skill development: College students’ perspectives on collaborative reading-to-write activities using a cloud-based tool
Bibliographic record
Abstract
Many students begin post-secondary studies with inadequate reading comprehension and expository writing skills. Strong skills in these areas are necessary for interpreting source material, integrating multiple ideas, and formulating clear and coherent arguments to complete integrated reading and writing tasks typical of most post-secondary assignments. This article reports on college students’ perspectives on an intervention study exploring how integrated reading and writing instruction can be combined with collaborative writing using a cloud-based tool to develop students’ expository writing skills. A 10-week intervention was designed to introduce 70 students to discrete expository writing skills such as paraphrasing, summarizing, and synthesizing source material. The intervention aimed to enhance students’ awareness of the connection between the individual skills and the final product: expository essays. This study was conducted at a large urban community college in Ontario, Canada, with culturally diverse first-year students across disciplines. Individual interviews were conducted with 14 students. Results of thematic and content analyses showed that participants were largely positive about their online collaborative writing experiences with their peers, and many found that the instructional design supported their learning through knowledge construction, social support, and easily accessible information.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".