The Relationship between Students’ Satisfaction in the LMS “Acadox” and Their Perceptions of Its Usefulness, and Ease of Use
Bibliographic record
Abstract
This study examined the relationship between students’ satisfaction in the newly developed Learning Management System “Acadox”, and their perceptions of its usefulness, and ease of use. The study used a descriptive correlational design to examine the relationships among variables, as they exist in their natural settings. To measure students’ satisfaction in Acadox, perceptions of usefulness, and perceptions of ease of use of Acadox, a web-basecd survey was developed be the researcher. Data were collected from seventy-five students enrolled in courses that used Acadox as a learning management system in the Middle East region, namely, Egypt and Saudi Arabia. The findings of this study revealed that students’ perceptions of ease of use, and perceptions of usefulness were significant predictors of satisfaction in Acadox. The findings of this study are consistent with the Technology Acceptance Model (TAM), which indicates that one’s perceptions of ease of use, and perceptions of usefulness of the new technology are the key factors that determine whether user will accept or not the new technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".