Using multimedia case studies to advance pre-service teacher knowing
Bibliographic record
Abstract
This paper uses Baxter Magolda’s (1992) framework on ways of knowing to examine the effects of using multimedia case studies with beginning pre-service teachers (PSTs). Baxter Magolda referred to these ways of thinking as absolute, transitional, independent, and contextual. The written responses to two sets of tasks were analysed for 36 PSTs enrolled in their first education course at a large private university. The first task had the PSTs watch parts of a multimedia case and then discuss what they saw with peers and a facilitator. The second task had the subjects interact and make sense of a different multimedia case individually. Using Baxter Magolda’s framework, each PST’s responses to the events were coded. Results indicate that working together PSTs operated within contextual ways of knowing more often than they did when working alone. Implications for teacher educators are discussed. Pre-service teachers, multimedia case studies, ways of knowing After making a visit to a local classroom to conduct her first observation, Michelle (all names are pseudonyms), a beginning pre-service teacher (PST), approached one of the authors and said, “I went to my school and watched Mrs. K’s class. I wrote down everything that happened. ” Michelle
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".