Impact of Emergency Remote Education in the 2021 COVID-19 Pandemic: A Case of Higher Education Students in the Department of Mechanical Engineering
Bibliographic record
Abstract
The higher education in Taiwan was forced to begin an unprecedented large-scale distance teaching emergently due to the outbreak of the COVID-19 pandemic in May 2021. This research accordingly intended to realize what the students had been experienced and the mechanical engineering at a private university of technology was investigated. There are five aspects surveyed and quantitatively their satisfying order from high to low is the following: learning resources, synchronous distance teaching, final assessment, learning effectiveness, and school equipment. Next, the qualitative data shows that most students embraced freedom and liberation at the beginning of the sudden implementation of the six-week emergency distance education, and soon discovered that the pressure of learning had become greater. Reasons include easy distraction at home, increased assignments, and relatively difficult online communication, etc. In summary, the participants have the highest satisfaction with "learning resources", although there are still students who complained that they can only access the visual classroom by poor facilities, like mobile phones (6% of the participants who cannot access computers/laptops for learning), old-fashioned computers, or no internet at home. It shows the limitations of unfair resources to online learning. Furthermore, the learning effect is the second-worst, showing that although the students have mastered the skills for synchronous remote education in the six weeks, most of them still prefer to return to the familiar and reliable face-to-face classroom, owing to the difficulties in communication, hardware, and software conditions. Finally, some suggestions for the college students to prepare for the uncertain future are provided based on the study conclusions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".