Time for a Change? A Research Update and Pilot Study Results on Academic Time-Based Decision-Making
Bibliographic record
Abstract
Engineering students in Canada and around the world are facing numerous challenges with their time, including absenteeism from class, surface approach to learning, and high stress levels. A research study to understand the time management habits of engineering students is ongoing and a pilot study has been completed. This paper contains an overview of the relevant background theories, the application of these theories to a new survey instrument, and the pilot study used to test and improve this instrument. The new survey instrument on time management and decision making was required as existing instruments published in the literature were considered to be flawed and inadequate for this study. The new instrument incorporates a decision-making dimension following the think-plan-do models of self-regulated learning theories. Analysis of this instrument will assume clusters, rather than factors, of time management behaviours to be the basis for grouping individuals. The pilot study was conducted on a number of self-selected graduate engineering students in November 2019. Participants filled out an online survey and then some volunteered for a think-aloud interview. Changes resultant from the pilot study analysis included question modifications, Likert scale modifications, and user experience improvements. The pilot study resulted in an overall improvement to the validity, reliability, and completeness of the survey instrument. The full study is currently being administered to undergraduate engineering students. The results will be published to help inform the manner in which time management is taught and used by engineering students.
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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 | 0.040 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".