Factors Influencing Students’ Decision to Drop Out of Online Courses in Brazil
Bibliographic record
Abstract
In recent years, e-learning has been the fastest growing educational form in students' numbers, and this industry's market revenue (Lee, Choi, &Kim, 2013). Despite this growth, concern about the significantly higher student dropout rate of students in online courses as compared with conventional learning environments has increased. Brazil has also registered a significant increase in the number of students interested in this type of education, but the dropout rate is a considerable concern to institutions. This study’s objective was to identify the relevant variables behind online students’ dropout decision in Brazil. After a literature review that determined the ten most recurrent and relevant variables, we heard professional e-learning experts. They indicated, from their standpoint, what the most pertinent variables influencing dropout would be. Based on this, we conducted a quantitative survey with e-learning students, considering the factors indicated in the literature on this subject and educational professionals’ indications. This study's contribution was to verify that the quality support is extraordinarily relevant and has a high correlation with students' perception of Usefulness, the quality of Course Content, and ease of System Use.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".