Translating and Validating the Community of Inquiry Survey Instrument in Brazil
Bibliographic record
Abstract
Massive open online courses (MOOCs) have emerged as an affordable way to distribute knowledge and democratize education. The examination of online courses calls for theoretical models and instruments that contemplate its particularities. The community of inquiry (CoI) framework has been used in several studies to analyze the effectiveness of online education and hybrid education, including MOOCs. This study aimed to translate and validate the Community of Inquiry Survey instrument (Arbaugh et al., 2008) into Brazilian Portuguese, and used a two-stage methodological design for translating and validating a questionnaire. In the first stage, we carried out translation, back-translation, and cross-cultural adaptation. We translated the 34 items while maintaining the survey’s original format. In the expert evaluation phase, all items were considered understandable and essential for inclusion in the Brazilian Portuguese version of the CoI instrument. In the second stage, a prospective cross-sectional study was conducted to validate the questionnaire, and data was collected from participants completing the Nursing Assessment MOOC available on the Lúmina platform. A total of 690 responses were gathered. The resulting instrument produced excellent results, and the three presences achieved high reliability indexes, clearly indicating their adequacy. Furthermore, this study proved the validation of the CoI instrument, maintaining the three-factor structure previously reported in the literature corresponding to the three presences: teaching, social, and cognitive presence. We recommend further studies to evaluate the need for excluding or altering cognitive presence items.
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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 | 0.064 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".