Factors that impact student usage of the learning management system in Qatari schools
Bibliographic record
Abstract
In an attempt to enhance teacher and student performance in school, a learning management system (LMS) known as Knowledge-Net (K-Net) was introduced in Qatari independent schools. (All public schools in Qatar have transformed to independent schools; the independent schools model is similar to the charter school system in North America.) An LMS is a tool that organizes and regulates classroom administrative tasks, supports teachers and students in the teaching and learning process, and informs parents of their children’s progress and school activities. Despite the benefits of the LMS, research studies indicate that its use by students has been limited because of a number of manipulative and non-manipulative factors that can influence behavior. This study explores the factors that impact student use of the LMS K-Net in Qatari independent schools. Quantitative data were collected through a questionnaire that was administered to students in 37 schools. A total of 1,376 students responded to the questionnaire. Semi-structured interviews were used to collect qualitative data that helped to confirm the results of the quantitative data and to provide additional insight on students’ perspectives regarding the use of the LMS. The results point to a strong relation between ICT knowledge and LMS usage. They suggest that the more ICT knowledge students have, the less prone they are to using the LMS. Attitudinal barriers were not predictive of usage. Student usage was strongly correlated to teacher and parent usage. This study is informative in evaluating LMS usage in Qatari schools. <br /><br />
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".