Factors Affecting Students’ Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School
Bibliographic record
Abstract
<p>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.</p>
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How this classification was reachedexpand
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".