Evaluation of Cognitive Loads Imposed by Traditional Paper-Based and Innovative Computer-Based Instructional Strategies
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".