The Flood Resilience Challenge serious role-playing game as an online education and engagement tool in a large engineering class
Bibliographic record
Abstract
Complex socio-environmental problems, such as flooding, cannot be adequately addressed through technical engineering solutions alone. Limited curricular opportunities exist for engineering students to understand and gain skills towards working collaboratively to address such problems. To enhance engineering students’ learning on flooding and engagement in the classroom, they participated in the Flood Resilience Challenge (FRC) serious role-playing game. This research investigates two main hypotheses. First, the FRC game is effective in helping students’ meet their learning objectives: (a) Increase flood literacy; (b) Better understand differing flood stakeholders’ views and power; and (c) Make connections between the game and real-life. Second, the FRC role-playing game will increase students’ engagement in the classroom through experiential learning. These hypotheses were tested using pre-game and post-game questionnaires, and a debrief. The findings suggest that the FRC game is an effective teaching-learning tool for enhancing engineering students’ understanding of the socio-environmental complexities of flooding issues. Students also reported that the FRC game, as an experiential learning tool, is more effective than traditional approaches to engineering education, such as lectures and readings. The research findings on the FRC game have relevance for a range of disciplines focusing on complex socio-environmental problems and as a tool to provide engaging online educational activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".