Factors Affecting Students’ Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School
Bibliographic record
Abstract
There is a substantial increase in the use of learning management systems (LMSs) to support teaching and learning activities in higher education institutions. Despite their benefits, students use them to a limited extend due to a number of factors influencing behaviors. This study executed a three-tier use model to examine the predictors of students’ behavioral intention to use LMSs at a two-year post-secondary military school in Turkey, and the participants consisted of 155 students. The data were collected through an online questionnaire and analyzed through Pearson correlation coefficients and linear regression analyses. Results showed that multimedia instruction had a direct influence on perceived usefulness and perceived ease of use, while interactivity had a direct influence only on perceived satisfaction. Perceived satisfaction was also affected by perceived usefulness and perceived ease of use. Perceived ease of use exerted a direct influence on perceived usefulness, as well. Moreover, perceived usefulness had a great influence on behavioral intention to use LMSs. As a final point, self-efficacy did not take a position in the model due to its insignificant relationships with the other constructs. It is suggested that an increase in the multimedia features and interactivity of the system could lead to higher perceived usefulness and ease of use among learners, and both factors add to learners’ perceived satisfaction which in turn increases their engagement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".