Teknologi Pendidikan: Pemanfaatan Teknologi dalam Pendidikan Pasca Pandemi
Bibliographic record
Abstract
The presence of the pandemic in the first quarter of 2020 also changed the order of human life. All activities that were originally carried out in person changed to online meetings using technological tools and digital platforms. This is no exception for the education sector. In the world of education, learning, which was originally carried out face-to-face, was replaced by online meetings. This lasted for at least a period of 2 years. With the change of learning space, it also changes habits so that it affects the quality of education. In this journal, we try to examine various kinds of changes that exist in the post-pandemic education world as well as the benefits and functions of educational technology from the pandemic era to post-pandemic. In this observation we use qualitative methods. Interviews are the data collection technique we choose. The technical spread is through a google form questionnaire, the results obtained from this study, namely the use of post-pandemic technology greatly affect the learning process. Post-pandemic learning provides benefits in the form of time and place efficiency. However, post-pandemic learning has not fully run well, because educators and students are less focused on learning sessions and interfere with students' understanding of the material presen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".