An initial review of learning analytics in Latin America
Bibliographic record
Abstract
Learning Analytics focuses on improving learning process by studying and analyzing data produced during the process itself. It covers the collection, measurement, analysis, reporting and knowledge discovering on data about students, teachers and institutions. Learning Analytics has been widely developed in Anglo Saxon countries. USA, United Kingdom, Canada and Australia are amongst the main contributors to this domain. Latin America is also starting to measure and optimize teaching and learning processes through Learning Analytics; however, the existing attempts in this direction are very isolated as there is a lack of a regional community to foster the interchange of ideas, methodologies, tools and local results in the field. The present work is a first attempt to identify Learning Analytics initiatives in Latin America by conducting a systematic mapping of papers from Latin American authors, and also by analyzing data about research groups from Latin America (collected through an open survey). In total, we categorized 30 articles published from 2011 until May 2016, and we analyzed data from 28 research groups that answered the open survey.
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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 | 0.000 |
| 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 | 0.001 | 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".