The Predictive Relationship Among the Community of Inquiry Framework, Perceived Learning and Online, and Graduate Students’ Course Grades in Online Synchronous and Asynchronous Courses
Bibliographic record
Abstract
<p>The Community of Inquiry framework has been widely supported by research to provide a model of online learning that informs the design and implementation of distance learning courses. However, the relationship between elements of the CoI framework and perceived learning warrants further examination as a predictive model for online graduate student success. A predictive correlational design and hierarchical multiple regression was used to investigate relationships between community of inquiry factors and perceived learning to determine the predictive validity of these variables for students’ course points (<em>N</em> = 131), while controlling for demographic and course variables. The results of this study clearly supported the foundational constructs of Community of Inquiry (CoI) theory (Garrison et al., 2000) and the role of perceived learning to predict final course points. The entire predictive model explained 55.6% of the variance in course points. Implications, limitations, and recommendations are discussed.</p>
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".