The Effects of Extraneous Load on the Relationship Between Self-Regulated Effort and Germane Load Within an E-Learning Environment
Bibliographic record
Abstract
Online instructors need to avoid unclear and confusing explanations of content, which can reduce the quality of learning. Extraneous load is reflective of poor instruction, in that it directs student effort towards processing information that does not contribute to learning. However, students may be able to manage poor instruction through effort regulation. Students who show high levels of effort have been shown to overcome poor instruction in some cases. This study analyzed survey responses from South Korean university students studying online (n = 1,575) to examine the relationship between self-regulated effort and germane load within varying extraneous load conditions. The experimental design separated extraneous load responses into three conditions (low, medium, high). Within each extraneous load condition, self-regulated effort responses were also separated (low, medium, high). The results showed that as extraneous load increased, self-regulated effort had a weaker relationship with germane load. It was also found that the use of effort regulation is effective only when dealing with low and mid-level extraneous load situations and that use of such strategies within high extraneous load situations was not effective. These results show the importance of improving instruction to reduce extraneous cognitive load, in that, not even high levels of effort can overcome poor quality instruction.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".