An Investigation of Metrics for the In Situ Detection of Software Expertise
Bibliographic record
Abstract
Task-based analysis is a common and effective way to measure expertise levels of software users. However, such assessments typically require in-person laboratory studies and inherently require knowledge of the user's task. Today, there is no accepted method for assessing a user's expertise levels outside of a lab, during a user's own home or work environment activities. In this article, we explore the feasibility of software applications automatically inferring a user's expertise levels, based on the user's in situ usage patterns. We outline the potential usage metrics that may be indicative of expertise levels and then perform a study, where we capture such metrics, by installing logging software in the participants' own workplace environments. We then invite those participants into a laboratory study and perform a more traditional task-based assessment of expertise. Our analysis of the study examines if metrics captured in situ, without any task knowledge, can be indicative of user expertise levels. The results show the existence of significant correlations between metrics calculated from in situ usage logs, and task-based user expertise assessments from our laboratory study. We discuss the implications of the results and how future software applications may be able to measure and leverage knowledge of the expertise of its users.
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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 | 0.008 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".