Simulated learners for testing agile teaming in social educational games
Bibliographic record
Abstract
This paper proposes an approach for creating and testing an multiagent systems based adaptive social educational game (SEG), QuizMASter, using the concept of simulated learners to overcome experimentation complexity and unpredictable student availability, as is typical with online learning environments. We show that simulated learners can play two roles. First, it can be used for testing the game planning, scheduling and adaptive assessment algorithms. With some degree of success met with our initial experimentation with QuizMASter, advanced planning and coordination algorithms are now needed to allow the gamebased assessment platform to realize its full potential. The multi-agent system approach is suitable for modeling and developing adaptive behaviour in SEGs. However, as we have found with our early prototypes, verifying and validating such a system is very difficult in an online context where students are not always available. MAS-based assessment game planning and coordination algorithms are complex and thus need simulated learners for testing purposes. Second, to overcome unpredictable student availability, we modeled QuizMASter as a new class of sociotechnical system, human-agent collective (HAC). In the system, human learners and simulated learners (smart software agents) engage in flexible relationship in order to achieve both their individual and collective goals, while simulated learners are selected for serving as virtual team members.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".