Towards Developing an Effective Algorithm Visualization Tool for Online Learning
Bibliographic record
Abstract
Although many Algorithm Visualization (AV) tools are available online, most of them are not effective in helping students to learn algorithm and data structures. The reasons for this may be the poor design consideration of the tools to fulfill the learners' needs in terms of pedagogy, usability, and accessibility. This paper reports a work-in-progress research project at Athabasca University on developing an effective algorithm visualization tool for online learning. We analyze the pedagogy, usability, and accessibility goals and their respective features for the online learners and examines how the visualization and user interaction principals can help to achieve these goals in the design process of an effective AV tool. We present a review of the related literature to highlight current and past researches and illustrate the need for better user experience design in AV tools. We then identify the online learners' needs and the context of their activities to design a usable, useful, effective, and pleasurable interactive AV tool. Such a tool 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".