Success factors for e-learning in a developing country: A case study of Serbia
Bibliographic record
Abstract
<p>In this paper, DeLone and McLean’s updated information system model was used to evaluate the success of an e-Learning system and its courses in a transitional country like Serbia. In order to adapt this model to an e-Learning system, suitable success metrics were chosen for each of the evaluation stages. Furthermore, the success metrics for e-Learning evaluation are expanded by providing several systems for quantifying the given success metrics. The results presented in this paper are based on courses that were taught both online and traditionally in three different subject areas: graphic design, information technology, and management. Of particular interest were success metrics which can be determined using quantifiable data from the e-Learning system itself, in order to evaluate and find the relationship between students’ academic achievement, usage of learning materials, and students’ satisfaction. The results from different courses were used to illustrate the implementation and evaluation of these success metrics for both online and traditional students.</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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".