Teaching Methods in Undergraduate Introductory Economics Courses: Results From a Sixth National Quinquennial Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents the first report of basic findings from the 2020 online administration of the sixth national quinquennial survey on teaching and assessment methods. Focusing on the teaching methods in introductory economics courses (i.e., principles and survey courses), the authors find that very little has changed in the past quarter-century. The typical instructor in introductory courses is predominantly a male, Caucasian, with a PhD. “Chalk and Talk” remains the preferred method of instruction in introductory courses, along with the use of textbooks. The use of “student(s) with student(s)” discussions in the classroom, as well as cooperative learning/small-group assignments, has increased since 2010. Lessons, activities, and references that address diversity, inclusion, or gender issues, however, are almost never used in introductory economics courses. JEL Classifications: A20, A22
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it