Integrating Perceived Added Educational Value Business Administration Core Course Items into Scales and Their Relationships to Degree Program Satisfaction and Business School Reputation Influence
Bibliographic record
Abstract
Prior research has not investigated perceived added education value in courses. Using a sample of 165 graduating business students, two business administration (BA) scales were created from six required BA core courses as part of students’ Bachelor of Business Administration (BBA) degree. Students were asked if each required course “added value to their education”. The two core scales (number of items) were labeled: BA Unique (4 items) and BA Generic (2 items). Analysis showed that the BA Unique scale had higher perceived added education value than the BA Generic scale. The BA Unique scale had stronger relationships to program degree satisfaction and Business School reputation than the BA Generic scale. These results supported the development of more unique required core courses based on business school stakeholder needs. Other schools should consider their stakeholders’ needs to see if more unique required core courses, beyond generic, are needed. Although only six of 21 required courses could be tested due to sample size limitations, these initial results suggest it is important to evaluate the perceived added education value of required courses in a curriculum.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".