Case studies, multimodal OERs and online collaboration: Enhancing undergraduate learners’ source-based expository writing skills in context
Bibliographic record
Abstract
Postsecondary students, including many English language learners, need effective skills in academic writing, especially source-based expository writing skills, to achieve academic success. However, research has shown that many first- and second-year undergraduate students are inadequately prepared and lack skills in both organizing writing to convey major and supporting ideas with critical perspectives and in reading comprehension. Limited research is available on addressing both skills with innovative instruction using digital technologies that appeal to these students. Therefore, this article draws on the relevant literature and my recent research projects with my team, which developed and examined the impact of two interventions on enhancing the source-based writing skills of students in a Canadian university and community college. It focuses on two key aspects that are of interest to this present volume, contextualization and socialization; that is, a case-study approach to providing contextualized writing instructions by using and developing multimodal open educational resources (OERs) and peer collaboration throughout the reading-to-write process employing a cloud-based platform.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".