Learning journals: creatively beginning the doctoral journey
Bibliographic record
Abstract
Purpose Creativity is not always encouraged in graduate education. It is established that PhD students need to think critically, but the emotional hurdles doctoral students face can sometimes outweigh the cognitive ones. In the authors’ work with doctoral students, the authors aimed to connect the emotional with the intellectual – the affective with the cognitive domains of learning, to provide students with the opportunity to make these links and better facilitate their progress. The authors used a learning journal that incorporated creative and critical thinking in an eight-month research course. The purpose of this study was to guide PhD students to become holistically involved in their doctoral journeys. Design/methodology/approach The authors conducted a self-study using a narrative methodology. Seven students kept learning journals throughout the course where they could write or draw to communicate their thoughts and feelings however they chose. The instructors provided written feedback to the students three times over the course. Afterword, students and instructors wrote narratives based on their experiences. The authors conducted a narrative analysis and produced a collective narrative. Findings Student experiences were variable; some more positive than others and often undergoing transformation during the process, but all students felt there was value in keeping a learning journal. Originality/value There are many studies on focusing on critical thinking for doctoral students but less on emphasizing the role of creative thinking.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".