Shove Less, Nudge More: Stakeholders’ Perspective from Writing Classrooms
Bibliographic record
Abstract
Academic writing courses are critical in higher education. However, they often rely on directive measures, or "shoves," that impose rigid guidelines, high-stakes assessments, and punitive consequences. These approaches, such as inflexible deadlines and harsh grading penalties, can increase student anxiety, disengagement, and surface learning. As a result, some students resort to unethical strategies, such as using essay mills or AI-generated content. This qualitative study, conducted through interviews with 20 writing professors and 30 students, identified several common shoves in academic writing courses and explored their negative impacts on student engagement and academic integrity. The findings highlight critical areas of concern, including strict rubrics, high-stakes deadlines, standardized feedback, and plagiarism threats. In response, the study proposes a shift from punitive shoves to supportive nudges, categorizing the latter into intuitive and didactic interventions. These nudges, such as automated deadline reminders, scaffolded assignments, and ethical AI usage prompts, aim to foster more positive student behavior and engagement. The next phase of this research will investigate how these behavioral nudges influence learning outcomes and student well-being.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".