Using PowerPoint in the Finance Classroom: Pros, Cons, and Recommendations
Bibliographic record
Abstract
The advent of personal computer technology in the last two decades has brought with it new opportunities for pedagogy. While the principles of good pedagogy are essentially timeless, the cosmetic delivery of the message through course files and web pages, intra-net servers, and presentation software has become more sophisticated and certainly more efficient. For those who are considering a switch to PowerPoint, this paper makes recommendations on getting up the learning curve efficiently, including recommendations on additional hardware and software devices that make developing and presenting easier. At the entry level, PowerPoint is hardly more than a page-based word processing program. However, using its advanced features, it provides a highly professional system for class note organization and electronic delivery that can dominate the standard tools (transparencies and blackboard) of prior decades for some types of teaching. Not surprisingly, the use of PowerPoint has strengths and weaknesses, depending on the teaching style the professor employs. PowerPoint is least effective in a case-based environment and most effective when it is used to produce course notes, which supplement or replace a textbook, or background bullet points for a lecture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".