Do research findings on schema-based instruction translate to the classroom?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Schema-based instruction has been shown to improve diagnostic performance and reduce cognitive load. However, to date, this has only been studied in controlled research settings. More distractions in classrooms may limit generalizability to real-world settings. We evaluated whether schema-based instruction would maintain its effects on cognitive load optimization and performance in a classroom. METHODS: Focused on the approach of interpreting cardiac auscultation findings, 101 first-year medical students at Western University were randomized to receive a traditional (n = 48) or a schema-based lecture (n = 53). Students completed four written questions to test diagnostic performance and a cognitive load assessment at the end of the lecture. Diagnostic performance and cognitive load were compared with independent t-tests. RESULTS: Schema-based instruction was associated with increased diagnostic performance on written questions (64 ± 22 % vs 44 ± 25 % p < 0.001) and reduced intrinsic cognitive load (mean difference = 15 %, standard error 3 %, p < 0.001). There was no significant difference in reported extraneous (p = 0.36) or germane (p = 0.42) cognitive load. CONCLUSIONS: Our results demonstrate that schema-based instruction can be used to reduce intrinsic load and improve diagnostic performance in a real-world classroom setting. The results would be strengthened by replication across other locations and topics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it