From Monologue to Dialogue: Creating a Community of Inquiry in Online Ecological Restoration Courses
Bibliographic record
Abstract
Universities are offering more online courses in ecological restoration to meet the growing demand for practitioners in this field. Online courses have the potential to contribute to this nuanced discipline by creating an environment in which learners from various backgrounds and locations can co-create knowledge. Unfortunately, online courses are often executed within the traditional educational paradigm of one-way knowledge transmission. In contrast, creating a community of inquiry invites learners to discuss ideas to generate meaning for themselves through collaborative and constructivist learning experiences. We tested techniques to create a community of inquiry in an online course in an ecological restoration program with the aim of shifting learners' interactions from the transmittal to constructivist model of knowledge construction. We changed three interdependent elements of communities of inquiry: the cognitive, social, and teaching presence. We measured participation and dialogue resulting from changes made to the 2014 and 2015 courses compared to the 2013 course, which served as the control. To quantify dialogue, we developed an index based on the different roles individuals take in a discussion. Results show that participation and dialogue increased, particularly in 2015 when the teaching presence followed a community of inquiry approach by showing curiosity, recognizing multiple perspectives and by illustrating that knowledge can be co-created. Variability in the results among groups suggested that some learners did not thrive in a community of inquiry. Nevertheless, many learners embraced collaborative knowledge construction, setting the stage for an enduring community of ecological restoration practitioners to emerge.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".