Measuring Time Load Using a Mobile Application to Monitor Curriculum Workload
Bibliographic record
Abstract
Insuffient time for learning activities makes learning very difficult. Weaker students need more time to appropriately manage their learning objectives. To ensure enough study time, curriculum designers must monitor potential mismatches between needed versus provided study time. This study was conducted to measure students' time loads and compare them to the workload determined by the curriculum and measured in European Credit Transfer and Accumulation System (ECTS) credits. Time load entry using the Studo mobile application consisted of entering the time required for all learning activities, categorized into attendance, self-study, and writing student papers, per course. In addition to time load measures, socio-demographic information on travel time, care obligations, and employment status was recorded. Over six semesters (2018/2019-2021), the average response rate per semester was low (8%-17%). Of the 75 piloted courses (4-16 per semester), 2 exceeded the number of hours specified in the curriculum. Regarding socio-demographic data, 3%-34% of the evaluated students worked part time (≥ 10 hours per week). In summary, students were disinclined to measure their learning time. With consideration of potential nonresponse bias, no significant evidence of curriculum workload exceedance was found for the evaluated courses at the University of Veterinary Medicine, Vienna. However, some students are under increased individual time pressure due to part-time employment. The ratio of measured to estimated time should be monitored as a key component to improve performance and enhance student learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".