Investigating the effect of learning management system transition on administrative staff performance using task-technology fit approach
Bibliographic record
Abstract
Educational institutions are adopting learning management systems (LMS) to facilitate teaching and learning processes. During the last few years, many Universities have started upgrading their existing LMS by shifting to advance LMS. This shift requires students, academic as well as administrative staff to get acquainted with the functioning of the new system at the earliest, as any change in the system may impact their performance. The transition from old to new LMS requires time and affects the performance of users, especially administrative staff performance. The present study tries to investigate the effect of the transition on the performance of the administrative staff. The task-technology fit (TTF) model was adopted as the theoretical framework for the study. The data analysis was done using the PLS-SEM, to test the hypothesized relationships. The findings of the study confirm that mere usage of the new technology did not improve the performance rather, the task and technology characteristics need to be coordinated appropriately.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".