Introducing a learning management system at a Russian university: Students' and teachers' perceptions
Bibliographic record
Abstract
<p class="BODYTEXT">Learning management systems (LMS) have been proven to encourage a constructive approach to knowledge acquisition and support active learning. One of the keys to successful and efficient use of LMS is how the stakeholders adopt and perceive this learning tool. The present research is therefore motivated by the importance of understanding teachers' and students' perceptions of LMS in order to anticipate possible issues (problems) and help to build a productive learning environment and a committed user community. The paper looks at this process at a Russian university (National Research University Higher School of Economics – HSE) where the system is being implemented and examines the following issues: qualification and readiness of the stakeholders to use LMS and their perceptions of the system's convenience, effectiveness, and usefulness. The research reveals remarkable divergence of students’ and teachers’ perceptions of various aspects of LMS which must be considered when raising the effectiveness of the system and building commitment to e-learning. They are analyzed and explicated in the present paper.</p><p class="BODYTEXT"><span lang="EN-US"><br /></span></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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".