Pre- and Early-Pandemic Graduating Students’ Perceptions at a United States Business School
Bibliographic record
Abstract
Two separate samples of graduating business students completed online surveys in the Spring 2019 (pre-pandemic, n = 724) and Spring 2020 (early pandemic, n = 376). This research study explored changes in student grading assessment learning perceptions (GALP) between Spring of 2019 to Spring of 2020, as well as perceptions of video vault use, number of internships/co-ops completed, satisfaction with major, and satisfaction with business degree. With the abrupt change from face-to-face to all online classes in the middle of the Spring 2020 semester due to the pandemic, individual engagement GALP (e.g., attendance, participation) declined but video vault use increased. Reassuringly, other GALP scales as well as both satisfaction measures remained stable. Testing for changes in correlations from 2019 to 2020, using the four GALP scales, video vault use, and internships/co-ops completed as the independent variables and satisfaction with major and satisfaction with business degree as the dependent variables, there were several significant correlational changes. The correlation of video vault use to satisfaction with major increased from 2019 to 2020. Increased Individual Creative GALP – satisfaction with business degree, and Individual Engagement GALP – satisfaction with business degree correlations were also found. The positive Spring 2020 video vault use findings, and maintained GALP scale perceptions were at least partially due to the immediate online Zoom faculty training facilitated by the Business School Online and Digital Learning Department. However, there was a negative correlation from 2019 to 2020 in the internship/co-op completed – satisfaction with business degree. . Study limitations and future research issues with the continuing pandemic are discussed.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".