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Improving the learning experience of decision support systems in entrepreneurship with 3D management simulation games

2022· dissertation· en· W7038625875 sur OpenAlexaboutno aff

Notice bibliographique

RevueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueHymenoptera taxonomy and phylogeny
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEntrepreneurshipBusiness simulationBusiness decision mappingPerceptionDecision support systemBusiness managementRepresentation (politics)Instructional simulation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Business simulation games are used in educational institutions and various industries in the private and public sector to train students and employees to practice the principles of management and decision-making skills by providing a fail-safe environment and enabling them to reflect on their simulation results. These games are generally advanced multiuser environments where a user, or a group of users, have access to a virtual company for making business decisions. Some of these games are expensive and their licences are time limited; typically, such a licence is only valid during the duration of the course. In general, these games are not available for the public as part of informal instructional courses. In Canada, teaching informal courses to immigrants and refugees, which involve data-driven decision making, to prepare them for future challenges they might encounter as business owners, can be challenging; especially considering language barriers and non-business-related backgrounds obtained outside of Canada. Furthermore, based on their decision-making styles, cognitive limitations, and past experiences, people may have an inaccurate perception of the problem or challenge they face, this could lead to poor decision making of the team they are part of and, therefore, this could reflect in the effectiveness of an organization as a whole. The objective of this research is to enrich current teaching tools in decision-making processes in entrepreneurship courses for newcomers in Canada with a comprehensive and visual representation of operational business problems involved in Business Intelligence and data analytics. More specifically, we designed and developed a 3D Business Simulation Game with randomized scenarios using modern technologies, such as Unreal Engine as the game engine; Adobe Fuse for the character creation, Mixamo for animation of the character, and Substance Painter for textures and materials for the assets. The research was conducted with the participation of the students of the Business Creation and Project Management course at VIRCS (Victoria Immigrant and Refugee Centre Society) where we tested this game on each one of the five units of the course. After designing, developing, and testing the 3D business simulation game, we conducted a comprehensive evaluation to investigate whether the decisions students made while playing were correct or not. We also evaluated whether they felt that the challenges were easier to understand, both as a team and individually, when they used the 3D business simulation game compared to only the written description of the problems. The main results we obtained from our study are the following: After playing the business simulation game, students became more aware of the importance of making correct decisions in different business scenarios. They made sure that the whole team understood the problem, and they felt generally good about their understanding of the course content. We also noticed that when the animation was not part of the business simulation game, they seemed to be confused when following written instructions. This indicate that they depended on the animations for their decision-making. We believe that, in some ways, the course and the 3D business simulation game we created for this research were a great opportunity to observe students becoming more confident in their future in Canada as entrepreneurs. We observed that, once the game has been used, the students become more participatory in class, the discussion of the course material increases, and in general, the students seem to enjoy the course more.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,025
Tête enseignante GPT0,258
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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