The Effect of Universal Design for Learning (UDL) Application on E-learning Acceptance: A Structural Equation Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Standardising learning content and teaching approaches is not considered to be the best practice in contemporary education. This approach does not differentiate learners based on their individual abilities and preferences. The present research integrates a pedagogical theory <em>Universal Design for Learning (UDL) </em>with an information system (IS) theory <em>Technology Acceptance Model (TAM).</em> It aims to examine the effectiveness of a technology-enhanced traditional web design course on blended e-learning acceptance and learner satisfaction in which UDL principles (multiple means of representation, action and expression, and engagement) were implemented. This casts some light on the role of addressing curricula limitations on learner perceptions and e-learning adoption. A mixed research design combining survey and action methods was followed. Overall, 92 undergraduate students took part in the study. The research instrument was validated first. Subsequently, partial least squares-structural equation modelling (PLS-SEM) was applied to identify the path associated among constructs used in the proposed framework. The extended model accounted for 45.4% and 41.6% of the variance of perceived satisfaction and behavioural intention respectively. The findings suggest that using educational technologies to address curricula limitations is a bridge to enhancing learner willingness to accept e-learning. </p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it