Bibliographic record
Abstract
This paper reconsiders the common use of mind maps as only a brainstorming tool that occurs before writing. The paper contemplates how mind mapping can be a useful pedagogical strategy throughout the writing process, not just at the beginning. The metacognitive benefits of mind mapping can support writers at all stages of their writing. Mind mapping can make their thinking overt and allow writers to make new connections throughout their revisions. The paper draws on an intrinsic case study (Stake, 2005) of sixteen first-year writing students who used mind maps at the beginning of their research papers and again as they grappled with feedback to re-design their drafts for submission. Students reported that, while the initial mind map had limited benefits on their writing, the second mind map acted as a vehicle for them to make connections between their draft, their feedback, and their next steps as writers. This second map offered a liminal space in which students could dwell with their feedback, make their thinking visible, and strategize how they could implement that feedback to make their writing stronger. The paper offers a new look at how teachers can use mind mapping to enhance students’ writing processes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".