Perception of Workload and its Relation to Perceived Teaching and Learning Environments among Finnish and Chinese University Students
Bibliographic record
Abstract
Earlier research has shown that an excessive workload has a substantial negative effect on students' well being. The present study examines how Finnish and Chinese university students' perceptions of workload are related to their perceptions of the teaching and learning environments (TLE). This is done in order to determine whether the perceived workload of students could be reduced by improving the quality of their TLE. Also the levels of experienced workload of Finnish and Chinese students are compared in this study. The group of participants consisted of 3035 Finnish students and 2309 Chinese students. Since this work is cross-cultural in nature, the adequacy of the level of structural equivalence of the research instrument was confirmed, and, when appropriate, the effect of different response styles on the results was taken into account. Both standard and robust statistical methods were used for the analyses. The results show that in both the Finnish and the Chinese groups the students' perceptions of their workload and TLE are significantly but rather weakly related. Furthermore, irrespectively of whether the response styles are accounted for or not, the results indicate that Chinese students perceive a heavier workload than do Finnish 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".