Comparing Linear and Nonlinear Delivery of Introductory Psychology Lectures
Bibliographic record
Abstract
As in most disciplines, the typical introductory class presents topics to students in a linear fashion, beginning (to use psychology as an example) with the history of the field, research methods, brain and neurons, sensation and perception, and so on. This study examined the impact of topic sequence on student achievement. The same professor taught two different sections of an introductory psychology university course during one semester. One section of the course received lectures in the standard linear order, while the other section received lectures in a nonlinear order, beginning with memory and consciousness, followed by history and research methods. Nonlinear-delivery students scored significantly higher on their midterm for four of the seven assigned chapters and performed better overall for the final examination than did linear-delivery students. Nonlinear-student attrition was also significantly lower (7.1% vs. 16.9%). These findings suggest that presentation of material in a nonlinear order better lends itself to student understanding.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".