Choosing a Business or Economics Study Program at University: The Role of the Economics Teacher
Bibliographic record
Abstract
The choice of a study program is based on complex individual decision-making processes. Thereby, economics is one of the most popular fields of study worldwide. Considering previous studies, the role of the teacher is often neglected. However, it can be assumed that teachers’ professional knowledge plays a significant role in a student’s choice of a study program. Thus, the present study investigated the influence of the professional knowledge that students perceive in their economics teacher on their aspirations and choice of an economics study program. The longitudinal data of 1387 Swiss high school students were analyzed. Economic competencies were measured multidimensionally and included knowledge, motivation, interest, value-oriented dispositions, and attitude. There were small to moderate correlations between the professional knowledge that students perceived in their economics teacher and their economic competencies. With regard to the intention and choice of economics, the results show small to moderate effects of the pedagogic content knowledge and the general pedagogic knowledge that students perceive in their teacher. These findings contribute to the discussion on the role of the economics teacher. It is therefore recommended that the teaching professionalism of economics teachers, which has been criticized in different countries, be promoted more strongly and more systematically.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".