The Development of Digital Content on the Metaverse Combined with Interactive Communication Activities with Professional on TikTok Marketing for Students
Bibliographic record
Abstract
This research aimed to 1) examine the needs for developing digital content on the metaverse 2) develop and evaluate the quality of digital content on the metaverse combined with interactive communication activities 3) compare the perception of the sample group before and after viewing the digital content, and 4) assess the satisfaction of the sample group with the digital content and activities. The tools used in the study include a needs survey, content and presentation quality evaluation forms, perception assessment, satisfaction assessment, and the digital content with interactive activities, which the researcher developed, consisted of 26 posters and 8 video clips. The sample group included 48 third-year students from the Department of Educational Communications and Technology, who registered for ETM 358 Marketing Communication in the second semester of 2023. Simple random sampling was used, selecting students who had previously viewed the content and were willing to respond to the survey. Statistical analysis involved mean, standard deviation, and t-test. The results showed that the sample group's demand for developing digital content with communication activities was at the highest level. Based on this, the digital content on the metaverse, combined with interactive communication activities with professional, was developed and evaluated by experts. The content quality was rated at a very good level, while the presentation quality was rated at a good level. Perception assessment after viewing the content and activities showed a significant improvement (p < .05), and satisfaction was rated at the highest level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".