Teaching EBP Using Game‐Based Learning: Improving the Student Experience
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is considered a key entry to practice competency for nurses. However, many baccalaureate nursing programs continue to teach "traditional" nursing research courses that fail to address many of the critical knowledge, skills, and attitudes that foster EBP. Traditional classroom teaching strategies do little to promote the development of competencies critical for engaging in EBP in clinical contexts. PURPOSE AND GOALS: The purpose of this work was to develop, implement, and evaluate an innovative teaching strategy aimed at improving student learning, engagement and satisfaction in an online EBP course. The goals of this paper are to: (1) describe the process of course development, (2) describe the innovative teaching strategy, and (3) discuss the outcomes of the pilot course offered using game-based learning. METHODS: A midterm course-specific survey and standard institutional end of course evaluations were used to evaluate student satisfaction. Game platform analytics and thematic analysis of narrative comments in the midterm and end of course surveys were used to evaluate students' level of engagement. Student learning was evaluated using the end of course letter grade. RESULTS: Students indicated a high satisfaction with the course. Student engagement was also maintained throughout the course. The majority of students (87%, 26/30) continued to complete learning quests in the game after achieving the minimum amount of points to earn an A. Seven students completed every learning quest available in the game platform. Of the 30 students enrolled in the course, 17 students earned a final course grade of A+ and 13 earned an A. LINKING EVIDENCE TO ACTION: Provide students with timely, individualized feedback to enable mastery learning. Create student choice and customization of learning. Integrate the use of badges (game mechanics) to increase engagement and motivation. Level learning activities to build on each other and create flow.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".