Assessing Understanding of Complex Causal Networks Using an Interactive Game
Bibliographic record
Abstract
Assessing people’s understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the logistics of this methodology may miss valuable and informative indicators of reductionist and linear thinking, both of which conflict with systems understanding.\nThis dissertation explores how interactive computer systems can aid in the assessment of causal understanding, allowing educators to perform more in-depth analysis of how subjects engage with the process of causal mapping. In addition, it considers how computer games as a particular form of interactive system may be able to support assessment. Games are framed as effectively supporting learning and education and although assessment is a key component of education, the use of video games for performing assessment is under-explored.\nTo address these topics, I present a prototype interactive game system based on Plate’s (2006) framework for assessing causal understanding through cognitive causal mapping. I tested this prototype in a user study with both student and non-student subjects. Through this study, I found that evaluating the structural forms of causal maps created in an interactive system can suggest the presence of reductionist thinking, while the sequence of causal map construction can indicate the presence of linear thinking. Furthermore, I found that although games as interactive systems can be effective in enabling learning, they may be less readily effective in supporting stand-alone\nassessments due to requiring an a priori understanding of the complex game system used in assessment, as well as traditional educational assessment contexts not supporting the forms of feedback critical to game-based learning.\nThese results indicate how the linear narratives prominently found in both education and games may interfere with effective systems thinking. This dissertation thus suggests that educators in both formal and informal education contexts should consider alternative, non-narrative curricula and games for teaching and assessing causal understanding of complex systems.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".