Understanding teaching in Polish MBA programs: a case study of perspectives of Polish academic teachers
Bibliographic record
Abstract
Polish Higher Educational (HE) programs are currently undergoing the process of reorientation to meet the changing societal, economic and educational needs and demands as a result of the globally transforming Polish context. Among those, MBA (Master of Business and Administration) programs have been the most dynamically developing area in the Polish HE system in the last decade. While their innovative structures and format continue to be the focal point of research during the transformation period, the nature of teaching in these new programs is not as yet documented. This descriptive and interpretative inquiry focuses, therefore, on the latter concern and explores some of the current conceptualizations of teaching and development of professionals in the context of Polish MBA programs. Based on a Canadian model, these programs claim to help students to prepare for the challenges of the transforming business sector by providing them with solid understanding of management issues, opportunities to develop a variety of managerial skills and professional values, as well as encouraging the process of their ongoing professional development. This inquiry explores how these issues, disregarded by Polish academic teachers prior to 1990 and viewed as critical during the post-1990 period, have been integrated into their teaching in MBA programs. This study describes and interprets how Polish academic teachers understand the role of MBA programs in the Polish context and adapt them to local needs. It provides insights into teachers' evolving perspectives on teaching, learning and professional development, as well as the altering classroom practices as influenced by their experiences in a variety of contexts and situations. Finally, it considers a wide array of factors and forces that either encourage or affect the quality of teaching in Polish MBA programs. The literature on teaching in higher education in the countries of the former Eastern bloc focuses primarily on describing changing educational structures as viewed from the perspective of external researchers, rather than its immediate participants. This inquiry fills this gap and emphasizes the understanding of teaching in MBA programs from the standpoint of their teachers and students. It contributes to a better understanding of various factors that influence the nature and focus of teaching in Polish MBA programs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".