Improving Multimedia Innovative Item Types for Computer Based Testing
Why this work is in the frame
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Bibliographic record
Abstract
Instead of computer games, animations, cartoons, and videos being used only for entertainment by kids, there is now an interest in using some of these media for educational purposes as well. Along with content creation, multimedia has potential for use in "innovative testing". Rather than traditional paper-and-pencil tests, audio, video and graphics are being conceived as alternative means for more effective testing in the future [1,17,21,28,29,30,33,42,44,49,50]. For example, we would like to use animation and games to help in learning concepts; consider how image, graphics and audio tools can be used for innovative testing; and develop techniques for measuring the impact of multimedia in improving performance or arousing interest in students. In this paper we discuss some examples of multimedia item types for testing, followed by a strategy for adaptive testing using those item types. We also show how techniques for perceptual evaluations can be used to improve strategies for adaptive testing
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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