Bibliographic record
Abstract
Traditional approaches to teaching large numbers of first year students business fundamentals require students to develop vocabulary and conceptual understanding of relationships across the core domains including economics, marketing, operations, finance, accounting, human resources, entrepreneurship, and information systems. We developed a new approach to building student knowledge by utilizing a data-driven approach where students interact with dashboards depicting the story of a Maple Syrup production company and a system where they compete to answer questions related to fundamental business concepts. SAP University Alliances member schools will have access to this curriculum when it is released. The concept of this arose from the idea that interacting with business data using easy-to-understand visualizations can accelerate learning of vocabulary and relationships in addition to improving data literacy and decision-making skills. Our team has been using data-driven approaches to teaching upper year analytics courses for several years. Upper-year analytics courses usually are taught following a CRISP-DM (Wirth, 2000) process, using iterative phases beginning with Business and Data and Data Preparation, and ending with Evaluation and Deployment. In this instance we flipped the process to begin with the last phases of Evaluation and Deployment to teach Business and Data Understanding. We developed simulated multidimensional business data for several products, employees and a story within the data relating to learning outcomes of four different scenarios which build upon each other. The data models were created in SAP Analytics Cloud and approximately 50 dashboard pages were created to be used in the 4 games. Each of the 4 games has about 30 questions programmed into the gamified Business Builders platform created by ERPsim Lab (https://businessbuilders.hec.ca/). Each of the 4 classes where we played the games lasted 80 minutes with an instructor led briefing, 30 minutes of 170 online students working with the Dashboards and Business Builders platform, and a debriefing. The first of the four games covered Sales and Marketing dashboards on Net and Gross Profit, Overhead and Direct Costs, Price Elasticity, and Marketing. The second game covered Finance and Accounting concepts, using three years of Income Statement and Balance Sheet data presented in Tables and Waterfall Charts following the IBCS best practices (Hichert, 2017). The third game covered Human Resources and Operations Management. We developed organizational charts and weekly payroll for ~50 employees as well as production and quality management dashboards. The fourth game covered Entrepreneurship, building on the business problems in the prior 3 stories with possible new product expansions that were assessed with dashboards looking into Pro-Forma Valuations and Net Present Value for different opportunities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".