Intelligent Games for Education - An Intention Monitoring Approach based on Dynamic Bayesian Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 | 0.001 | 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 it