Visualizing a Hierarchical Taxonomy in a Children’s Web Portal: User Evaluations of Two Prototypes
Bibliographic record
Abstract
Elementary students use the Web to find information, but can encounter problems with keyword searching. An alternative is to choose terms from a taxonomy (subject directory), but students may then encounter problems in locating a term within the taxonomy. This paper reports on a comparative analysis of a conventional, hierarchically displayed taxonomy with a display of the same taxonomy using information visualization techniques. The evaluations were undertaken by students from grade-six (11 to 12 years’ old) and are part of a larger study on the application of information visualization techniques to interfaces targeted at children.Les élèves du primaire utilisent le Web pour trouver de l'information, mais peuvent avoir des difficultés avec la recherche par mots-clés. Une possibilité serait de choisir des termes à partir d'une taxonomie (liste de sujets), mais dans ces circonstances, ils pourraient avoir de la difficulté à localiser le terme voulu. Cet article présente l'analyse comparative d'une taxonomie hiérarchique conventionnelle et de la même taxonomie présentée en utilisant des techniques de visualisation de l'information. Les évaluations ont été effectuées par des élèves de 6e année (11 et 12 ans) et font partie intégrante d'une étude plus vaste sur l'application de techniques de visualisation de l'information dans la conception d'interfaces ciblant les enfants.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".