Is a general extended technology acceptance model for e-learning generalizable?
Bibliographic record
Abstract
e-Learning acceptance has received considerable attention in the educational technology literature. In recent years, many frameworks have been proposed, modified, and applied to better understand the factors underlying students’ acceptance of e-learning. Despite the important progress made with the acceptance literature, extant empirical examinations have unfortunately often produced discordant findings. Researchers frequently advance situational factors as possible moderating influences on technology to explain the high degree of variance unexplained in specific technology acceptance situations. Generalized models have been proposed that attempt to integrate situational variables to account for the high degree of situational variability that occurs across technology acceptance contexts. Abdullah and Ward proposed such a general extended technology acceptance model in the context of e-learning (GETAMEL). In the current paper, our objective is to quantitatively evaluate the GETAMEL, and consider it with respect to a situative perspective on technology acceptance in order to more fully characterize the dynamical relationships and situational factors influencing determinants of e-learning acceptance. This study, drawing on a survey of 132 college students, validates the GETAMEL employing a partial least square path modeling approach.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".