Seeing the Forest Through the Traits: A Four-Dimensional Framework for Modeling and Measuring Latent User Traits
Bibliographic record
Abstract
Understanding latent user traits is critical for designing adaptive systems that personalize effectively and responsibly. However, most existing methods rely on lengthy psychometric instruments or focus narrowly on observable behaviors, limiting their scalability and psychological depth. In this paper, we introduce a novel framework for modeling user traits using four interpretable, higher-order dimensions—Perceptual, Relational, Exploratory, and Self-Agency. We derived these dimensions from an empirical analysis of 16 latent traits across personality, cognitive ability, and perceptual skill. We demonstrate how this dimensional structure, refined via dimensionality reduction and clustering, enables interpretable user modeling without sacrificing coverage. To support efficient user assessment, we develop adaptive diagnostic questionnaires that follow path-based decision trees, reducing the number of items while maintaining strong internal consistency and classification performance. A path-level inter-survey analysis on the Self-Agency dimension shows high convergence with the original Rotter Locus of Control scale (r = 0.849, p < .001). Finally, in an initial application study, we show how this framework improves the prediction of task effort in data visualization contexts, outperforming a user-agnostic model in both cross-user and cross-visualization generalization settings. Together, our contributions offer a modular, scalable approach to capturing latent user traits for adaptive interaction design.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".