Improving Learning Outcomes: Unlimited vs. Limited Attempts and Time for Supplemental Interactive Online Learning Activities
Bibliographic record
Abstract
Research indicates the use of interactive online learning (IOL) instructional strategies such as multiple choice, "drag and drop" matching exercises, and case discussions, in online courses enhances learning and results in better learning outcomes. While some instructors might use interactive resources for regular assessments that only allow for one attempt, this experiment examines whether limiting the attempts and the time to complete IOL instructional strategies significantly improves learning outcomes as measured by performance scores on two required exams. The author posit that students who have limited attempts (2) and limited time (20 minutes) will in fact read the chapters before attempting to complete the interactive online activities, thus increasing the correlation between the interactive online activity scores and exam scores. Unlimited attempts and unlimited time provide students with the opportunity to search the textbook for the answers without reading the assigned chapters.As anticipated, the experimental groups with limited attempts and limited time on the IOL activities did demonstrate a statistically significant relationship to combined exam scores. The findings indicate that limited attempts and limited time on formative assessments correlated with exam scores while those formative assessments without constraints did not.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".