Recall or transfer? How assessment types drive text-marking behavior
Bibliographic record
Abstract
Introduction Text marking is a widely used study technique, valued for its simplicity, and perceived benefits in enhancing recall and comprehension. This exploratory study investigates its role as an encoding mechanism, focusing on how marking impacts recall and transfer when learners are oriented toward different posttest items (recall or transfer). Method We gathered detailed data describing what learners were studying and how much they marked during studying. Participants were randomly assigned to one of four groups in a 2 × 2 factorial design. One independent variable, examples, determined whether participants were trained using examples of the types of information required to answer posttest items. The other independent variable, orientation, determined whether participants were instructed to prepare for a recall test or for an application (transfer) test. Results Statistical analysis revealed a detectable effect of study orientation (transfer vs. recall), F = 2.076, p = 0.043, partial η2 = 0.114. Compared to learners oriented to study for recall, learners oriented to study for transfer marked information identified as examples (F = 3.881, p = 0.051, partial η2 = 0.028), main ideas (F = 7.348, p = 0.008, partial η2 = 0.051), and reasons (F = 5.440, p = 0.021, partial η2 = 0.038). Moreover, a statistically detectable proportional relationship was found between total marking and transfer performance (F = 5.885, p = 0.017, partial η2 = 0.042). Learners who marked more scored higher on transfer questions. Prior knowledge mediated approximately 52% of the effect, indicating that as prior knowledge increased, so did the frequency of marking. Discussion Orienting to study for a particular type of posttest item affected studying processes, specifically, how much learners marked and the categories of information they marked. While the frequency of marking was proportional to achievement, orienting to study for recall versus transfer posttest items had no effect on recall or transfer. Prior knowledge powerfully predicted how much learners marked text.
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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 | 0.003 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".