Factors affecting social networks acceptance: An extension to the technology acceptance model using PLS-SEM and Machine Learning Approach
Bibliographic record
Abstract
Once the university started using social media more, the researchers started focusing more on how social media applications were being adopted and what motivated it without being limited to classrooms only. There is a need to conduct further research about how the utilization of social media to teach in university affects education. Considering this, delving deeper into the educational outcomes and a research model related to the experiences and results of social media use is the aim of this research. Apart from that, the Technology Acceptance Model (TAM) research that deals with the behavior intention of using social networking media, perceived playfulness, perceived ease of use and perceived usefulness has been used for testing what affects the utilization of social media for online-teaching in higher education of United Arab Emirates. There was an assessment of 580 quantitative responses given by university students whose classroom sessions involved using social media. In order to predict the behavioral intention of a pupil for using social networking media for e-learning in the higher education institutions, it is possible to take some help from the factors such as perceived playful-ness, perceived ease of use and perceived usefulness, as per the partial least squares (PLS) and machine learning evaluation. The suggested model helps teachers to get to know more about how classroom sessions can become more productive through social media usage.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".