Evaluation of Cognitive Loads Imposed by Traditional Paper-Based and Innovative Computer-Based Instructional Strategies
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Bibliographic record
Abstract
Strategies of presenting instructional information affect the type of cognitive load imposed on the learner's working memory. Effective instruction reduces extraneous (ineffective) cognitive load and promotes germane (effective) cognitive load. Eighty first-year students from two veterinary schools completed a two-section questionnaire that evaluated their perspectives on the educational value of a computer-based instructional program. They compared the difference between cognitive loads imposed by paper-based and computer-based instructional strategies used to teach the anatomy of the canine skeleton. Section I included 17 closed-ended items, rated on a five-point Likert scale, that assessed the use of graphics, content, and the learning process. Section II included a nine-point mental effort rating scale to measure the level of difficulty of instruction; students were asked to indicate the amount of mental effort invested in the learning task using both paper-based and computer-based presentation formats. The closed-ended data were expressed as means and standard deviations. A paired t test with an alpha level of 0.05 was used to determine the overall mean difference between the two presentation formats. Students positively evaluated their experience with the computer-based instructional program with a mean score of 4.69 (SD=0.53) for use of graphics, 4.70 (SD=0.56) for instructional content, and 4.45 (SD=0.67) for the learning process. The mean difference of mental effort (1.50) between the two presentation formats was significant, t=8.26, p≤.0001, df=76, for two-tailed distribution. Consistent with cognitive load theory, innovative computer-based instructional strategies decrease extraneous cognitive load compared with traditional paper-based instructional strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it