Transformation of digital communication: Students’ timely graduation model in blended learning post COVID-19 pandemic
Bibliographic record
Abstract
The shift in learning communication patterns in higher education emerged along with the end of the pandemic, accelerating the transformation of digital communication. This study aims to explain the digital communication variable of the CMC model, the digital technology variable of the technology and determinism model, the SIKA LMS variable of the technology acceptance model (TAM), the discipline variable of the Attitude and Behavior theory model, and the graduation variable of the Media Dependence model as part of the communication using the computer. Quantitative research method with SEM PLS analysis. Furthermore, the data collection technique was used with a proportionate stratified random sampling population of 3416 students and a sample of 302 active students working on the final project. The analysis in this study uses Structural Equation Modeling, with 5 variables, namely digital communication (X1), digital technology (X2), SIKA LMS (X3), student discipline (X4) and timely graduation (X5), conducting outer model tests, goodness of fit models, and model testing inner. Results show that the discipline variable obtained the highest average of 4.22. In contrast, the digital communication transformation obtained a very significant direct influence on the shift in learning patterns for timely graduation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".