An investigation of computer generated knowledge retention activities in computer-based training with adult learners
Bibliographic record
Abstract
The goal of this investigation was to evaluate the impact of training and theeffectiveness of different types of knowledge retention activities delivered bycomputer-based training programs. This study focused on a computer-based learningsystem called the Profound Learning Delivery System (PLS). PLS is an application designed to improve the content knowledge retention of adult learners who arecompleting computer-based training. This study used a pretest-posttest experimental design to compare adult learners’knowledge of Microsoft Outlook ("Outlook," 1997) before and after a computer-basedtraining session. Participants were trained using two different computer-basedinstructional programs; a commercially available software program matched forcomparison purposes and PLS. This comparison involved three different formats forpost-instruction retention activities that were; no review activities, user generatedreview activities, and program generated retention activities. Results indicate, therewas a significant difference between the groups 60 days after training. This resultdemonstrated that PLS has potential worth exploring.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".