Predicting the intention to use google glass: A comparative approach using machine learning models and PLS-SEM
Bibliographic record
Abstract
Technology-based education is the modern-day medium that is widely being used by teachers and their students to exchange information over applications based on Information and Communication Technology (ICT) such as Google Glass. There is still resistance shown by a few universities around the globe when it comes to shifting to the online mode of education. While few have shifted to Google Glass, others are yet to do so. We base this study to explore Google Glass Adoption in the Gulf area. We thought that introducing the teachers and students to all the pros that Google Glass presents on the table might get their attention in considering using it as the medium to exchange information in their respective institutes. This paper presents the structure of a framework depicting the association between TAM and other Influential factors. All in all, this investigation analyzes the incorporation of the Technology Acceptance Model (TAM) with the major features associated with the method such as instructing and learning facilitator, functionality, and trust and information privacy to improve correspondence among facilitators and students during the learning process. A total of 420 questionnaires were collected from various universities. The data that was gathered through the surveys was employed for the analysis of the research model using the Partial least squares-structural equation modeling (PLS-SEM) and machine learning models. The outcome showed that the factor of functionality and trust and privacy goes hand in hand with perceived usefulness and perceived ease of use associated with Google Glass. Both the Factors, Perceived usefulness and perceived ease of use have a significant impact on Google Glass adoption. This implies the significant impact of Perceived ease of use and Trust and privacy on the adoption of Google Glass The study also offers practical implications of outcomes for future research.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".