Multimedia and games incorporating student modeling for education
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
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.
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 | 0.000 | 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 | 0.000 | 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 it