Examining the Foundation: Scaffolding “soft” skills from entry to graduation in an undergraduate business program
Bibliographic record
Abstract
Critical thinking, creativity, communication, teamwork, emotional intelligence, problem-solving, empathy, resilience, ambition, grit, innovation... (Heckman & Kautz, 2012). These skills, known as a contentious four-letter “S” word (soft), are considered a requirement for employment and advancement for the 21st-century graduate (Carnevale & Smith, 2013). Within a School of Business, in an environment highly regarded for technical skill achievement in diploma and degree graduates, faculty set out to investigate the contentiousness and inclusion of human skills in curriculum outcomes. While not directly built into the curriculum, there is an institutional understanding that human skill development is innately a part of the programs. The intention is that human skills (LeBusque, 2020), or power skills (PMI, 2022) naturally occur during course delivery, creating a commonality across foundational courses to reinforce the skill sets identified, developed, and refined as students complete their credentials. However, industry reports (Lapointe & Turner, 2020; RBC, 2019) and the authors’ own institutional data collected from new graduates and employers indicated room for improvement in these skills. To gain a better understanding, the authors undertook a critical examination through a document analysis of all common core courses that form the program foundation. This involved 24 common courses, comprising 1,442-course objectives or outcomes, resulting in a range of 134 Bloom’s Taxonomy verbs. In this paper, the authors begin the first phase of this comprehensive study with a return to the course foundations. Through this analysis, the authors present a framework to better understand and strengthen the learning foundation, to proceed with realignment and strategic scaffolding of both technical and human skills from entry through graduation. Keywords: business education, skill development, course objectives, Bloom's Taxonomy, 21st-century graduate, document analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".