Perceptions of Business Students toward Online Education before and in Transition Period of COVID-19
Bibliographic record
Abstract
Covid-19 affects our lifestyles dramatically. It also affects the education styles. Spurred by the Covid19 pandemic, most of the learning in a traditional classroom setting were transferred to online format. This study was designed to assess the students’ perception on the traditional and online learning before Covid-19 pandemic and in transition to the Covid pandemic when all classroom learning is closed and transferred to online based learning. Students were sampled to obtain their general perceptions regarding traditional and online learning. The data were collected via an online survey during October/November 2019 and March/April 2020. Findings indicate that the perceptions of students changed negatively in transition period. A large majority of students agreed before Covid-19, but they disagreed in transition period on the following: online education increases learning levels; students learn more with online courses; zoom is much better than learning in the classroom environment; online courses are easier than traditional courses; I would recommend taking online courses instead of in class courses to a friend or colleague; and I would like to take my other courses in online form. The results show that a sudden change to the system will negatively impact the students and it did not allow time for students to adjust to the change. They are simply not ready to take all the courses online. However, students all agreed the use of technology in classroom increases their engagement and interest in the subject matter
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".