The Association of Retrieval Practice and Sleep Consolidation with Student Grades in a Large Undergraduate Human Gross Anatomy Course
Bibliographic record
Abstract
It has been shown that information retrieval through repeated practice testing, as opposed to repeated studying, is an efficient way of enhancing information retention and recall. In addition, it has been shown that sleep, a few hours after learning, enhances future information retrieval. However, there is very little research that implements these study strategies into large undergraduate classes under conditions that would exist naturally within a class setting and determines the influence they may have on student grades. We implemented both retrieval practice and sleep consolidation strategies into the course content of a large, third year undergraduate course in human gross anatomy and analyzed the association of each with student exam and final course grades. Retrieval practice occurred at the end of most lectures and at the beginning of each lab session, while sleep consolidation occurred once a week, the night before course laboratories. Students read the course notes covered throughout the previous week and were assigned practice questions to answer as they read through the material. The questions were not retrieval questions (ie. they were done as students read their notes) but simply a means of confirming that students had read through the material. Students received course credit of up to 5% for participating in each of the three components (lecture retrieval, lab retrieval, sleep consolidation). With data collection ongoing, correlations will be run to assess the relationship between retrieval practice and sleep consolidation with exam and final course grades. Multiple regression analysis will determine whether there is an additive predictive influence of retrieval practice and sleep consolidation on student grades. This will allow us to test whether participation in retrieval practice and sleep consolidation offers an advantage over retrieval practice alone. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".