Does the Effectiveness of Interspersed and Blocked Questions Vary across Readers?
Bibliographic record
Abstract
ABSTRACT This paper examines whether answering questions that are interspersed within a reading versus presented as a block after a reading produces different learning outcomes for different readers. In an initial study, financial accounting students who read with interspersed (blocked) questioning earned higher exam scores if, in other courses, they typically read before (after) class. A follow-up study randomly assigned students to either interspersed or blocked questioning when reading about business viability. Interspersed questions led to greater delayed recall by stronger readers, whereas blocked questions led to greater delayed recall by weaker readers. A third study randomly assigned type of questioning (interspersed or blocked) and reading conditions (mind-wandering induced or not) to students learning the revenue recognition model. Test scores were higher when interspersed questions were asked of students not induced to mind-wander and when blocked questions were asked of students induced to mind-wander.
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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 | 0.012 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".