Intelligent Games for Education - An Intention Monitoring Approach based on Dynamic Bayesian Network
Bibliographic record
Abstract
Computer games have become one of the preferred choices for entertainment in our society primarily because they are interactive, have appealing multimedia content, and provide an immersive and rewarding environment for players. These qualities constitute an essential psychophysical factor that inspires learning abilities and new knowledge. Despite all these promising elements, studies have shown that current educational games are not as effective as they could be. A lack of adaptive tutoring and feedback tools, lack of proper knowledge assessment, and weakly designed gameplay are the major factors for their inefficiency.We address these problems by proposing an Intelligent Tutoring System (ITS) for computer games. An important contribution of this ITS is its capability to track player intentions and award partial marks, which provides more accurate assessment than simply giving full mark to the correct result and none to an incorrect answer. Two strategies adopted in this system are Bayesian Networks based student modeling and individualized tutoring. The system can incorporate one or more games and can address one or more educational topic. The information collected from student interaction with computer games is used to update a student module that reports a students current level of knowledge, making adaptive tutoring and assessment with computer games more effective. In order to provide an engaging and interactive environment, each game in the system has a local student module constructed based on a Dynamic Bayesian Network. We describe the design and evaluation of our ITS using a prototype implementation with several game examples. Positive evaluation results support the feasibility of the proposed system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".