Investigating Time Estimation From a Self-Regulated Learning Perspective
Bibliographic record
Abstract
The present study investigates university students' time estimation accuracy from a Self-Regulated Learning perspective.Specifically, the study examines students' goal quality, competence for goal completion, and perceptions of goal difficulty as predictors of time estimation accuracy for single study session at three points over a semester.An additional goal of this study was to investigate the relationship between time estimation accuracy and students' reported goal completion.Results show that more than 50% of students underestimated or overestimated their time to complete goals at every time point over the semester.Results of multinomial logistic regression analyses demonstrated that perceived goal difficulty was a predictor of underestimation at the middle and at the end of the semester, competence for goal completion predicted time estimation accuracy at the beginning of the semester, and goal quality was not a significant predictor of time estimation accuracy at any point in the semester.Lastly, students who overestimated the time spent in their study sessions were less likely to attain their goals.These results provide empirical evidence of the prevalence of misestimation during individual study sessions guided by goals created by students for course-relevant tasks and partial support to theoretical principles of SRL which consider task perceptions and goal setting as determinants of the learning process.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".