Not Everything Important Is Taught in the Classroom: Using Cocurricular Professional Development Workshops to Enhance Student Careers
Bibliographic record
Abstract
Business schools and graduate business programs have struggled since their inception to ensure that what students learn in their courses will resonate with the skills needed in their careers. To date, there has been limited attention paid to cocurricular experiential learning opportunities to assist with this challenge. In this article, we discuss the process of implementing cocurricular professional development (PD) workshops as part of a college-wide initiative to increase experiential learning opportunities for students. This college-wide initiative challenged two assumptions: first, that the classroom is the best space for valuable learning and second, that faculty are the best equipped to lead decisions on what students should learn. The workshops help students develop both the tangible and intangible skills required to succeed in industry. We faced many challenges in implementing the workshops, including the need to challenge the predominant view that nothing of significant value could be learned in a workshop. We conclude our article by identifying the factors responsible for the ultimate success of the program and offer guidance for colleges looking to change perceptions of value in learning and broaden their experiential learning practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".