Extending The Community of Inquiry Framework: Development and Validation of Technology Sub-Dimensions
Bibliographic record
Abstract
Since the mandatory switch to online education due to the COVID-19 outbreak in 2020, technology has gained more importance for online teaching and learning environments. The Community of Inquiry (CoI) is one of the validated frameworks widely used to examine online learning. In this paper, we offer an extension to the CoI framework and survey, arguing that meaningful and appropriate use of technologies has become a requirement in today’s pandemic and post-pandemic educational contexts. With this goal, we propose adding three technology-related sub-dimensions that would fall under each main presence of the CoI framework: (a) technology for teaching, (b) technology for interaction, and (c) technology for learning. Based on exploratory and confirmatory factor analyses, we added 5 items for technology for teaching sub-dimension, 4 items for technology for interaction sub-dimension, and 5 items for technology for learning sub-dimension in the original CoI survey. Further research and practice implications are also discussed in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".