Preliminary Do not quote A Comparison of Online and Face-to-face Learning in Undergraduate Finance and Economic Policy Courses
Bibliographic record
Abstract
This paper addresses some of the questions about the effects of technology on student learning. Using data from students enrolled in a Canadian business school during the 2005 summer term, we compare the performance of online and face-to-face students in two different undergraduate courses: Economic Problems and Policy Analysis and Basic Corporate Finance. When controlling for a potential selection bias and other variables that may have an effect on students ' performance, we find that online students perform better than face-to-face students in economics, while there is no such differences in finance. This is in sharp contrast with previous studies (Brown and Liedholm, 2002, Anstine, J. and Skidmore, 2005, and Coates, et. al., 2004) which showed that online students perform significantly worse than live students. As in other studies, we find that cumulative average has a positive effect on the results in both economics and finance, and that women have significantly lower results in economics than men. 1.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".