Perceived Utility and Learning by Dominican University Students in Virtual Teaching–Learning Environments: An Analysis of Multiple Serial Mediation Based on the Extended Technology Acceptance Model
Bibliographic record
Abstract
The global pandemic caused by the SARS-CoV-2 virus brought about a true revolution in the predominant teaching–learning processes (i.e., face-to-face environment) that had been implemented up to that point. In this regard, virtual teaching–learning environments (VTLEs) have gained unprecedented significance. The main objectives of our research were to define an explanatory theoretical model and to test a multiple serial mediation model with four variables in series (one independent variable plus three mediators) to relate perceived utility (independent variable) in the use of a VTLE and perceived learning (dependent or criterion variable) in such contexts, taking into account the mediation of subjective norm (mediator 1), ease of use (mediator 2), and intention to use behavior (mediator 3), and using the extended technology acceptance model as the theoretical framework. Additionally, we aimed to analyze the direct and indirect relationships and effects among the variables that constituted the proposed model. Methodologically, the research can be classified as a cross-sectional causal ex post facto design. A representative sample of students enrolled in higher education institutions in the Dominican Republic was used as the research population, and a standardized Likert scale was administered to measure the five dimensions of the proposed model. Finally, it is worth noting that the obtained results indicate that all direct and indirect effects considered in the model were statistically significant, except for the indirect effect, where the four predictor variables were arranged in series to verify their influence on the criterion variable: perceived learning.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".