Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities
Bibliographic record
Abstract
Abstract In Latin American universities, Learning Analytics (LA) has been perceived as a promising opportunity to leverage data to meet the needs of a diverse student cohort. Although universities have been collecting educational data for years, the adoption of LA in this region is still limited due to the lack of expertise and policies for processing and using educational data. In order to get a better picture of how existing data‐related practices and policies might affect the incorporation of LA in Latin American institutions, we conducted a mixed methods study in four Latin American universities (two Chilean and two Ecuadorian). In this paper, the qualitative data were based on 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students; the quantitative data were collected through two surveys answered by 1884 students and 368 teachers, respectively. The findings reveal opportunities to incorporate LA services into existing data practices in the four case studies. However, the lack of reliable information systems and policies to regulate the use of data imposes challenges that need to be overcome for future LA adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".