Multimedia and games incorporating student modeling for education
Bibliographic record
Abstract
With the wide availability of online social and educational websites, there is now an interest in using online multimedia for education. Rather than traditional paper-and-pencil tests, audio, video and graphics are being conceived as alternative means for more effective testing in the future. In this presentation we review some examples of multimedia and games for testing. Computer games have become one of the top choices for entertainment in our society. Computer games are interactive, have appealing multimedia content, and provide an immersive and rewarding environment to the player. These qualities are 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 badly designed gameplay are the most significant factors for their inefficiency. We propose to address these problems by using an Intelligent Tutoring System for computer games. The main components of this system are a student model based on Bayesian Networks and a multi-level tutoring model. The system is composed of one or more games based on a single or multiple educational topics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".