Structural Equation Model: an Analysis of Learning Management Systems Acceptance
Bibliographic record
Abstract
The continuous growth of ICT in the last decade is transforming the traditional model of teaching and learning based on face-to-face master classes. Today there are virtual online educational platforms that allow students and teachers to interact virtually and use multimedia resources from any mobile device or computer with Internet access. The transition from presence to virtuality can generate resistance to change, this situation must be analyzed to take strategies that allow the effective implementation of virtual educational platforms by teachers and students. The aim of this paper was to identify the aspects that influence the use behavior of learning management systems (LMS), based on data from an online survey sent to 250 students of systems engineering. This research analyzes the impact of five constructs; platform operation, planning and scheduling, teaching program contents, methodology and competencies of teachers, communication and interaction and allocation and use of media resources with use behavior. This paper concludes that the platform operation, planning and scheduling, communication and interaction, the allocation and use of media resources are the constructs that more influence the use behavior of LMS regardless teaching program contents and competencies of teachers.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".