Factors That Influenced Post-Secondary Faculty Members' use of a D2L Learning Management System
Bibliographic record
Abstract
In 2019, the college administrators at a large 2-year college located in Canada implemented a Learning Management System (LMS) policy for all instructors. The goal of the LMS policy was for faculty members to demonstrate committed professionalism and high degree of competence in teaching. The problem was that instructors underutilized the LMS to provide feedback to students, make course content available in a variety of accessible formats to students, and promote student engagement in learning. In this study, the factors that influenced instructors’ use of the LMS in accordance with the LMS usage policy were explored. The expanded technology acceptance model grounded this study. The research questions were designed to explore how faculty members from the School of Business perceived that the system quality, their self-efficacy and facilitating conditions influenced their use of the LMS in accordance with the LMS policy. A basic qualitative study was conducted, and 11 faculty members employed by the School of Business were interviewed. Data were analyzed using open coding followed by axial coding. The results revealed that participants perceived system quality, self-efficacy and facilitating conditions influenced their use of the LMS in accordance with the LMS policy. The study findings led to the development of a policy paper for administrators at the School of Business that made recommendations regarding interventions to improve faculty member professional practices and the overall student experience. Positive social change could result from the administrators using these recommendations to provide interventions that will improve instructor use of the LMS to provide learning opportunities so that students will become successful learners.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".