Revolutionizing Learning Environments with Guerrilla Pedagogy in Large Classes
Bibliographic record
Abstract
Engaging students in large classes can be challenging for educators. In this study, we implemented a guerrilla tactic in an effort to engage our students. Guerilla tactic is a pedagogical approach where one teacher (the “guerrilla”) enters into a colleague’s class that is in session, sits for a while, takes over the teaching for about ten minutes, then leaves the classroom. There is an element of student surprise with guerrilla pedagogy because students are not informed in advance about the guerrilla visit and the host instructor has no prior knowledge on what the visiting guerrilla instructor would talk about. For this study, two practical nursing instructors who teach the same courses (i.e., anatomy and physiology, and pathophysiology) to different sections collaborated as guerrilla instructors. Four sections of students; two from anatomy and physiology and two from pathophysiology participated in the study. Each section had about one hundred students. The disruptive guerrilla pedagogy was implemented during the 2019 winter semester. At the end of the semester, students completed a survey about their experiences that had both Likert scale and open-ended questions. The instructors critically reflected on their experiences. Thematic analysis and descriptive statistics were used to analyze the data. Overall, students and the instructors had positive experiences with the instructional strategy. In our reflective analysis, we answer Hutchings's (2000) taxonomy of scholarship of teaching and learning (SoTL) inquiry questions. We found that students appreciated being exposed to two experts who have different instructional strategies. Educators have to trust and respect their peers in ways that allow them to be vulnerable and enhance their practice. The surprise and instructor collaboration brought by guerrilla pedagogy enhanced students’ engagement in large classes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".