The Effect of Universal Design for Learning (UDL) Application on E-learning Acceptance: A Structural Equation Model
Bibliographic record
Abstract
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 Universal Design for Learning (UDL) with an information system (IS) theory Technology Acceptance Model (TAM). 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.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".