Using Digital Media During the COVID-19 Pandemic Era: Good Online Program in Higher Education
Bibliographic record
Abstract
This study aims at documenting the experience and perceptions of an Indonesian university professor in regard to teaching using digital media during the coronavirus disease 2019 (COVID-19) pandemic. Ample research has pointed out that the use of digital technologies can raise both potentials and challenges. This study examines the two contrasting perspectives by considering the current health disaster, the COVID-19 pandemic, which can add to the complexities of the virtual education in Indonesia. Research on virtual edu-cation in the context of Indonesian higher education during the pandemic is very limited and, thus, this study has gained its significance. We used qualitative methodology to approach this investigation with interview as the data collection technique and thematic analysis as its method of analysis. The results of this study present some key insights into the ways to integrate digital technologies within higher education instruction and what criteria to consider when selecting digital media. We argue that using digital technolo-gy helped educators facilitate teaching and learning regardless of the health crisis they were facing. This paper can be of use for educators in higher education to find ways in infusing digital media in their everyday instructions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".