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 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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.044 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".