<i>Once upon a time….</i> Storytelling to Enhance Teaching and Learning
Bibliographic record
Abstract
PURPOSE: The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. METHODS: A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). RESULTS: Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. CONCLUSIONS: Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".