Learner Analysis to Inform the Design and Development of a Serious Game for Nongaming Female Emerging Health Care Preprofessionals: Qualitative Sample Study
Notice bibliographique
Résumé
BACKGROUND: Overall, 75% of health care practitioners are women, but half of all females do not play digital games of any kind. There is no consensus in the literature regarding optimal design elements to maximize the efficacy of serious games. To capitalize on the promise of serious games in health care education, it is important for instructional designers to understand the underlying learners' values, attitudes, and beliefs that might motivate nongaming female health care preprofessional students to independently choose to persistently play serious games to mastery. OBJECTIVE: Specifically, the aim of this study was to seek answers to 2 questions. First, what values, attitudes, and beliefs contribute to the nongaming behaviors of 12th-grade female emerging health care preprofessionals? Second, how do the values, attitudes, and beliefs of 12th-grade female emerging health care preprofessionals align with important design features of serious games? METHODS: In this study, a learner analysis was conducted using semistructured interviews with 8 12th-grade college-bound female health science students to better understand learners' values, attitudes, and beliefs to inform the design and development of a serious game. These interviewees represented a diverse subset of the female emerging health care preprofessionals who self-identified themselves as not playing games at all, not very often, or infrequently. RESULTS: The findings suggest that the study participants exhibited a complex fusion of desire for both accomplishment and affiliation. The participants were all independent, competitive, and prosocial leaders. They thought strategically and consciously self-limited their leisure time to achieve personally meaningful long-term goals. They embraced overcoming expected failures and aimed to achieve relevant high-stakes wins in all academic, athletic, extracurricular, and leisure activities they valued while consciously avoiding what they considered to be non-goal-oriented activities. CONCLUSIONS: The results of this study reinforce the need for a robust learner analysis to identify the multifaceted behavioral characteristics of targeted learners before the design and development of serious games. The common characteristics of the 12th-grade female health science students in this study suggest that they will choose to invest their limited leisure time playing a personally meaningful, preprofessionally authentic serious game if the collective design elements are aligned with the students' self-conceptualization of their present or future selves.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».