Learner Analysis to Inform the Design and Development of a Serious Game for Nongaming Female Emerging Health Care Preprofessionals: Qualitative Sample Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".