University student’s goal profiles and metacomprehension accuracy
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
In this study, undergraduate students provided confidence ratings to predict future performance in answering questions drawn from the text before reading the text, after reading the text and after rereading the text. Self-reports of achievement goal orientations during reading and posttest scores were also collected. Student’s calibration index was the comparison between their predicted posttest performance and actual performance in the posttest. Correlational analyses did not reveal any statistically detectable relationships between self-reported goal orientations and monitoring accuracy, except that bias scores were marginally related to goal orientations. Further cluster analyses and analyses of variance (ANOVA) also showed that student’s multiple goal profiles failed to clearly differentiate the groups in terms of their calibration accuracy, yet performance-approach goals did distinguish overconfident from underconfident students. Plausible reasons for the finding were provided and implications for future research were also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.005 | 0.001 |
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