Establishing Knowledge and Cultivating Talent via Experiential Learning: The Case of the Fashion Retail Lab
Bibliographic record
Abstract
Purpose In this teaching innovation, “ The Boutique” , the students are guided through the experiential learning cycle: experiencing, reflecting, thinking and acting. The assignment requires individuals to take on retail roles (i.e., operations, planning & buying, communications, training and development, accounting, and merchandising) to execute desired business outcomes. While studies of retail labs in the past have been limited to exploring the physical elements of a store, this assignment highlights techniques in which social media campaigns can be used to enhance retail goals. This innovative real-life lab experience thereby responds to the challenge that retail educators face encounter to get students to actively integrate ongoing digital strategies with actual retail operations. Method/Design and Sample The retail lab learning experience was analyzed through students’ qualitative comments using directed content analysis. This input was compared against the course’s learning outcomes. Results Students reported that the lab enhanced their digital and social media knowledge, teamwork skills, real world application, collaboration capabilities, understanding of sales and profits, and engagement with the consumer community. Value to Marketing Educators The paper benefits marketing educators by providing a blueprint of a retail lab that helps students to develop communication and teamwork skills, decision-making abilities, and forge career paths. In particular, the exercise builds students’ digital and social media knowledge. We also outline 8 learning outcomes to further students’ learning goals.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".