Digital undervisning - här för att stanna?
Bibliographic record
Abstract
In March 2020 the novel coronavirus COVID-19 forced Swedish universities to transfer from face-to-face learning to digital education. From one day to another, students could no longer interact with other students and teachers in a traditional classroom. The drastic change from face-to-face learning to digital education creates a unique situation and an interesting phenomenon to investigate. From a communicative perspective, we want to contribute to the research field of strategic communication. The research purpose of this study is to investigate in what way students’ internal communication satisfaction has been affected by digital education at Swedish universities. To fulfil the purpose, we conducted a quantitative survey with 221 respondents at Swedish universities and analysed the data through means and multiple regression analysis. The models and measurements used in this study are Communication Satisfaction Questionnaire, CSQ, Media Richness Theory, MRT, and Technology Acceptance Model, TAM. Findings show an average internal communication satisfaction level at 3,134 out of 5. We also found that Media Richness has a positive effect on students' internal communication satisfaction at Swedish universities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.024 |
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".