Um Estudo do Uso de Contagem de Interações Semanais para Predição Precoce de Evasão em Educação a Distância
Bibliographic record
Abstract
Distance Learning (DL) is a modality of education mediated by technology, which students and teachers usually interact through a Virtual Learning Environment (VLE).With the growth of this type of education arise new challenges and opportunities in learning and computation, related to a better use of available resources.One of the key features present in the DL is the availability of data generated by user interactions.This data can contain records of each user interaction, including date and time of these interactions, as well as its location and type.Thus, you can use this data to discover and model the behavior of different types of users.This article describes the initial results of a work targeted to early prediction of students' dropout in a distance learning course, using data mining about their interactions in the initial 4 weeks of the course.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".