Affordance-based User Personas : A mixed-method Approach to Persona Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the last decade, the persona technique has been used in interface design practices to put user needs and preferences at the center of all development decisions. Persona development teams draw on qualitative data, quantitative data, or a combination of both to develop personas that are representative of the target users. Despite the benefits of both methods, qualitative methods are mostly limited by cognitive capabilities of the experts, whereas quantitative methods lack richness. To gain the advantages of both methods, this paper suggests a mixed-method approach to create user personas based on the pattern of affordances they actualize, rather than merely the actions they take. It enriches personas by referring to the purposes fulfilled through affordance actualizations, and grounds personas in readily available objective log data. This study illustrates the practical value of the proposed methodology by empirically creating student personas using the Moodle learning management system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it