Analysing users’ satisfaction with e‐learning using a negative critical incidents approach
Bibliographic record
Abstract
One critical success factor for e‐learning is learners’ satisfaction with it. This is affected by both positive and negative experiences in a learning process. This paper examines the impact of such critical incidents on learners’ satisfaction in e‐learning. In particular, frequent occurrence of negative critical incidents has significant potential of negatively affecting satisfaction. The focus of this paper is on assessing satisfaction with e‐learning from a ‘negative critical incidents’ perspective. The paper describes a satisfaction assessment model, called SAFE. The results of an empirical study at the National Sun Yat‐sen Cyber‐University are used to evaluate and validate the SAFE model. Based on the results, the critical incidents that affect e‐learning satisfaction are classified into four categories: administration, functionality, instruction and interaction. Of these, interaction and instruction are found to be the most important factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".