Research in Writing Instruction and Assessment: Current and needed research to improve student writing
Bibliographic record
Abstract
Discussions among educators at almost any level will invariably result in one point of agreement: students at all levels are under-prepared in writing skills. Unfortunately, this is a conclusion that also predominates much of the research literature on the improvement of student writing as well. Despite the importance attached to high-stakes academic writing skills, research has contributed little insight about the challenges students face with academic writing tasks. Llosa, Beck, and Zhao (2011) point out that the National Commission on Writing in America’s Schools and Colleges found that this lack of understanding of the writing process was so significant that they identified writing as, the “Neglected ‘R’ (National Commission on Writing, 2003; Llosa, Beck and Zhao, 2011). In the following paper, authors Caldwell and Outcault Hill present a broad review of the areas of research into the writing process and assessment of writing and suggest areas where further research is needed. Their discussion focuses on 1) Research related to the influence of Cognitive function on the writing process, 2) Research into teaching various genres such as exposition, argument, narrative, analysis, and creative writing, 3) Research on the assessment of writing, and finally, 4) Research on alternative teaching methods.
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 | 0.090 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".