Bibliographic record
Abstract
Abstract Interactive graphics are commonly used in games and have been shown to be successful in attracting the general audience. Instead of computer games, animations, cartoons, and videos being used only for entertainment, there is now an interest in using interactive graphics for ‘innovative testing’. Rather than traditional pen‐and‐paper tests, audio, video and graphics are being conceived as alternative means for more effective testing in the future. In this paper, we review some examples of graphics item types for testing. As well, we outline how games can be used to interactively test concepts; discuss designing chemistry item types with interactive 3D graphics; suggest approaches for automatically adjusting difficulty level in interactive graphics based questions; and propose strategies for giving partial marks for incorrect answers. We study how to test different cognitive skills, such as music, using multimedia interfaces; and also evaluate the effectiveness of our model. Methods for estimating difficulty level of a mathematical item type using Item Response Theory (IRT) and a molecule construction item type using Graph Edit Distance are discussed. Evaluation of the graphics item types through extensive testing on some students is described. We also outline the application of using interactive graphics over cell phones. All of the graphics item types used in this paper are developed by members of our research group.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.011 |
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".