Harmonizing Teaching Tools with Cognitive Learning Outcomes in the Teaching of Economics
Bibliographic record
Abstract
The selection of teaching tools is a key determinant of the extent to which the anticipated learning outcomes of a course will be realized. As such, choosing optimal teaching tools can be a greatly effective course of action to enhance learning in the classroom. As Terregrossa et al. (2009) point out, “it is ironic that the practitioners of the discipline devoted to the study of efficiency principles [i.e. economics] are implicitly accused of being inefficient in their approach to teaching that discipline.” The purpose of the present paper is to first explain cognitive learning outcomes as well as review both traditional and modern teaching tools in the context of economics. Next, the appropriate teaching tools that match correspondingly with each specific cognitive learning outcome are proposed in the setting of teaching economics. To this end, the paper concentrates on Benjamin Bloom’s (1956) taxonomy of cognitive domains to describe different cognitive learning levels. Then, a diverse set of teaching tools suitable to teach economics are corresponded to different cognitive learning outcomes. More specifically, the present paper aims to introduce different teaching tools - including course formats, major teaching methods, and teaching moves - corresponding to different levels of cognitive domain in the context of teaching economics. Finally, it is argued that economics instructors should select teaching tools as well as contents, readings, in-class activities, assignments, and assessment formats after formulating the learning outcomes of the course, so that the teaching tools selected can facilitate students’ learning and help them achieve the anticipated learning outcomes more readily.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".