No Time Like the Present: Recommendations for Educators to Improve Engineering Student Time Management and Decision Making
Bibliographic record
Abstract
A research study investigating the connections from time management & decision making to various outcomes in engineering education has been completed. A synthesis of study results and literature has resulted in several recommendations, with associated specific actions in four implementation categories: professors, students, programs, and researchers. The results uncovered a wide variety of time management strategies which may be successfully employed, with the most important aspect being having control over one’s time. Anecdotal implementation of one recommendation — keeping deadlines within a standard “workweek” — appears to have had noticeable positive results at another institution. Although the study was conducted on engineering students, the results may be applicable to a wider university audience, especially those with high levels of classroom instruction and/or deliverables.
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.
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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".