Bibliographic record
Abstract
This paper presents a work-in-progress project for developing an open interactive algorithm visualization (AV) website. This development of project is “open”, because we are developing this website as part of our University's Open Education Resources (OER) initiative. This is “interactive”, because we aim to incorporate interactive functionalities to meet pedagogy, usability, and accessibility needs of the online leaners. In our previous research, we did a detailed survey on the teaching and learning methodologies of the algorithms and data structures and the existing AV websites. Although most AV websites are good at providing some fancy graphics and animations, they did not pay much attention on the learners' needs in pedagogy, usability, and accessibility. We identified that the leaners pedagogy, usability, and accessibility needs are the three important areas that should be bringing into design considerations for an ideal AV website design. In this work-in-progress paper, we present the current state of our open interactive AV website development and the plan for the future to address learners' load management through multimedia functionalities in the AV website. Such amendment in the AV website can fulfill the pedagogy, usability, and accessibility goals for the online learners.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".