Behaviourist-Constructivist Pedagogical Design Possibilities Within the Community of Inquiry Framework
Bibliographic record
Abstract
The discourse on blended learning must not take away focus on learning by concentrating explicitly on technologies. When learner-centred discussions are incorporated, these may be confined to constructivist pedagogies, as evident through the well-established community of inquiry (CoI) framework. While a few early advances argue for behaviourist pedagogies to underpin the CoI framework in particular, and a plethora of literature supports behaviourist-constructivist interplay for blended learning in general, this study pioneers the proposal of these interactions within the CoI framework for blended learning. It also challenges the prevailing stand-alone socio-constructivist pedagogical design of the CoI framework to deal with the complexities of higher education by adopting a decolonial positionality. In this light, we explain the impact of missing out on behaviourist designs on the CoI framework through the problem of epistemological untenability and that of assumed learning. Having provided the rationale for including behaviourist designs, we then emphasize the behaviourist-constructivist interactions within the framework. This discussion paper contributes to the ongoing dynamic scholarship of the CoI and encourages the research community to empirically explore the positioning of such a pedagogical design within this framework.
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 | 0.055 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".