Advocating for blended pedagogy as a shift to more holistic inclusive geography
Bibliographic record
Abstract
Applied geography represents the dialectic relationship between theory and application (Pacione 1999 Pacione, M. (1999). Applied geography: in pursuit of useful knowledge.Applied Geography,19(1), 1–12. https://www.sciencedirect.com/science/article/pii/S0143622898000319[Crossref] , [Google Scholar], 2004 Pacione, M. (2004). The principles and practice of applied geography. In Applied Geography (pp. 23–45). Springer, Dordrecht. https://link.springer.com/chapter/10.1007/978-1-4020-2442-9_3[Crossref] , [Google Scholar]). A well-informed rigorous pedagogy must represent this dialectical relationship (Pacione 1999 Pacione, M. (1999). Applied geography: in pursuit of useful knowledge.Applied Geography,19(1), 1–12. https://www.sciencedirect.com/science/article/pii/S0143622898000319[Crossref] , [Google Scholar]). How then can the relationship between the theoretical and the applied be challenged within the “spaces” of the university? I provide a critical discussion of the use of a blended learning pedagogy (Davis & Fill 2007 Davis, H. C., & Fill, K. (2007). Embedding blended learning in a university's teaching culture: Experiences and reflections. British Journal of Educational Technology,38(5), 817–828. https://bera-journals.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-8535.2007.00756.x[Crossref], [Web of Science ®] , [Google Scholar]) in challenging that there is a separation of theoretical and applied geography. A blended learning pedagogy allows for the inclusion of emerging media and learning technologies as tools in developing a new “space” for teaching and learning, a “space” which is focused on active learning. The walls of the university lecture room become porous, as students begin to make critical connections between theory and application. I unpack the pedagogical justification for a shift to a blended learning approach. I support this justification with evidence from curriculum design in a first-year geography course, and the student experience of implementation of this blended learning approach. I assess student engagement with theory through the integration of real-world case studies into a blended delivery approach. I evaluate their critical engagement by applying a mixed-method approach to a survey and follow-up focus group.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".