Measuring the Impact of Innovations in Bertheussen’s ‘Digital School Examinations’
Bibliographic record
Abstract
This paper reviews Bernt Arne Bertheussen’s recent article published in this journal which offers a promising new approach to teaching undergraduate finance courses. Bertheussen (2014) presents a comprehensive approach which argues that a greater emphasis on the use of spreadsheets satisfies the students’ desires to develop increased experience, familiarity and skill in working with information and communication technology. Such a practice, suggests the author, would also nurture a deeper, more enduring understanding of the underlying finance concepts covered. The current review of this innovation offers Bertheussen substantial credit for an important innovation in finance pedagogy. However, this review does draw attention to and analyzes the substantial limitations in the approaches that Bertheussen utilizes to measure the effectiveness of this innovation. Alternative approaches to gauge the effectiveness of this new teaching method which might serve to persuade more instructors of the utility of such a pedagogical advancement are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".