Using Dashboards to Teach Business 101
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».