How Career-Relevant Problem Based Learning in a Virtual Environment Can Improve Student Writing
Bibliographic record
Abstract
Writing proficiency is critical for management professionals but is often underemphasized in business curricula. We examine how career problem-based learning (CPBL) activities, like salary negotiations and career-pitches, enhanced management students’ writing competencies. Undergraduate business students (N=156) engaged in CPBL activities designed to mimic real-world scenarios and received targeted feedback, incentives, and practice opportunities. We evaluated improvements in writing by using instructor grades and metrics inferred from a dictionary derived from a large language model (Linguistic Inquiry and Word Count, LIWC, 2022). We found that students used more descriptive, definitional, argumentative, insightful, and analytical language and less alluring language by the end of the semester. Overall, role-playing and weekly writing activities significantly improved the writing skills of management students. The results and course design highlight the potential of CPBL activities in integrating skill-based learning with career-oriented activities to enable them to succeed in their professional lives.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".