A Confirmatory Factor Analysis of the Oddi Continuing Learning Inventory (OCLI)
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
Oddi's 24-item Continuing Learning Inventory (OCLI) is an instrument that has been used frequently to measure self-directed learning. Although three previous studies have assessed OCLI's underlying dimensions, the conflicting results of those studies prompted this further investigation that used a series of factor analyses of the 250 responses from University of Toronto undergraduate medical students. Although exploratory factor analyses yielded results consistent with Oddi's empirically derived three-factor model, further analyses of the student responses suggested that OCLI's underlying dimensions are better described by four factors. This four-factor model was also identified through confirmatory factor analyses. These four underlying OCLI dimensions—Learning With Others, Learner Motivation/Self-Efficacy/Autonomy, Ability to be Self-Regulating, and Reading Avidity—provide further specificity and insights for examining and better understanding students' self-directed learning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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