Utilization decision towards LMS for blended learning in distance education: Modeling the effects of personality factors in exclusivity
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
Over the decades, personality factors (attitude, self-efficacy, anxiety and computer experience) have pervaded the underpinning determinants of behavioural intentions to accept and use emerging technologies, chiefly in purviews where integration is into the working processes that may be pro traditional. The chasm in the literature has been how these technology personality factors extensively relate within and among themselves in a definite model exclusive to these factors, and their overall variance explained in usage intentions. In view of this, the study adopted a quantitative design and employed the questionnaire for data collection from 267 distance education tutors from a countrywide spread. Findings from structural equation modeling (SEM) technique revealed ‘technology attitude’ and ‘technology experience’ to be major predictors of usage intentions. The direct effects of technology anxiety and self-efficacy on behavioural intention were fully mediated by technology attitude. Non-linear relationships showed that technology self-efficacy, experience and anxiety were all antecedents of attitude towards LMS, while ‘technology experience’ alone determined ‘technology self-efficacy’. The Important-Performance Map Analysis (IPMA) revealed attitude as the most important and performing construct in determining behavioural intention. Technology attitude had technology related self-efficacy as its most important and performing construct determinant. The overall variance explained by the derived model was 35%. The study recommended that technology attitude and experience should be prioritized in LMS-related blended learning implementation in distance education. It further proposed that future studies include moderators on technology personality factors in determining usage intentions to further improve the model’s robustness.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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