Power Point in Legal Education: Pedagogical Paradox-An Exploratory Study
Bibliographic record
Abstract
This article is based on a more detailed paper prepared as part of the requirements of the doctoral program under the supervision of Professor Toni Williams.The author would like to thank all of the professors and students at Osgoode who responded to his surveys as well as the library, information technology, and records staff who made the necessary data collection possible.1 When I refer to PowerPoint use, I am referring to the projection of computer-generated slides onto a screen as part of a lecture.While a variety of software is available, Microsoft PowerPoint is the predominant program used at Osgoode Hall Law School. 2 See for example, William R. Andersen, "Administrative Law Discussion Forum: Computer Graphics in the Teaching of Administrative Law" (2000) 38 Brandeis L.J. 229. 3 See for example: James Eagar, "The Right Tool for the Job: The Effective Use of Pedagogical Methods in Legal Education" (1997) 32 Gonz.L. Rev. 389 4 The first version of PowerPoint was limited to the production of overhead transparencies.Ian Parker, "Absolute Powerpoint: Can a software package edit our thoughts?"New Yorker (28 May 2001) 76 at 80.There is no significant difference in student performance between lectures using PowerPoint as distinguished from lectures using overheads.See Attila Szabo & Nigel Hastings, "Using IT in the Undergraduate Classroom: Should We Replace the Blackboard with PowerPoint?" (2000) 35 Computers & Educ.175.See also C. Ahmed, "Powerpoint versus Traditional Overheads.Which is More Effective for Learning?"(Paper Presented to the Conference of the South Dakota Association for Health, Physical Education and Recreation, November, 1998) [unpublished].However, as Szabo and Hastings note, there may be case specific instances where PowerPoint is more effective (ibid.at 187).
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".