Evaluation of Learning Outcomes Through Multiple Choice Pre- and Post-Training Assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Training programs, in industry, are a common way to increase awareness and change the behavior of individuals. The most popular way to determine the effectiveness of the training on learning outcomes is to administer assessments with Multiple Choice Questions (MCQ) to the participants, despite the fact that in this type of assessment it is difficult to separate true learning from guessing. This study specifically aims to quantify the effect of the inclusion of the ‘I don’t know’ (IDK) option on learning outcomes in a pre-/post-test assessment construct by introducing a ‘Control Question’ (CQ). The analysis was performed on training conducted for 1,474 participants. Results show a statistically significant reduction in the usage of the IDK option in the post-test assessment as compared to the pre-test assessment for all questions including the Control Question. This illustrates that participants are learning concepts taught in the training sessions but are also prone to guess more in the post-test assessment as compared to the pre-test assessment.
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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 | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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