What to do? A Review of the Academic Time-Based Decision-Making Literature
Bibliographic record
Abstract
Time-based activities at Universities are shifting toward a more transactional approach, yet there is little understanding of the time management capabilities of students in adapting to a more flexible structure. Although many studies report on efforts to address engineering students being stressed, surfacelearning oriented, and prone to missing class, few studies address how these relate to students’ time management. In an effort to explore how students value, prioritize, and spend their time, this paper proposes a new term, “Academic Time-Based Decision-Making” (ATBDM), which lies at the crossroads of time management, selfefficacy, and self-regulated learning. Factors influencing ATBDM are currently mostly speculative, although class scheduling, social norms, and the internet and social media are frequent causal suggestions. It is also unknown as to how ATBDM is conducted across the breadth of students, which skills or “tools” are employed, and whether the process or influencing factors change over the course of time. A research study to explore why and how engineering students make academic decisions is proposed. By providing deeper insights into the factors influencing ATBDM, it may be possible to develop more effective support or intervention to assist students in making balanced and positive choices.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".