Beyond the Lecture: A Flipped Class Approach to Paralegal Education
Bibliographic record
Abstract
In Canada, paralegal education lacks comprehensive pedagogical research in effective instructional models. Historically, paralegal programs focus on clerical skills, neglecting higher-level legal comprehension and analytical abilities that are vital for a paralegal role in the workforce. This research addresses this gap by exploring the implementation of a flipped classroom approach in an Alberta university that offers paralegal education. This study evaluated whether a flipped classroom in a legal technology course could enhance engagement and understanding of fundamental legal principles among paralegal students, compared to lecture-based models in other previously experienced university courses. Survey data collected through a mixed methods approach in April 2024 revealed that most participants believed that the flipped classroom encouraged participation, felt more confident in applying legal concepts, and were better prepared for the workforce. By examining the impact of flipped classrooms on paralegal education, this research provides insights that can inform curriculum development, address paralegal training challenges, and ensure the acquisition of necessary competencies for success in the legal industry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".