Does Supplementing IS Analysts’ User Observations With Hands-on Training Help Them Better Understand Users’ Work?
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
IS analysts need to acquire knowledge about users’ work processes to design high-quality systems. While researchers have proposed hands-on activities in cognitive learning theories to improve knowledge acquisition, current approaches rely on analysts verbally communicating with users or observing them perform their tasks in order to learn these work processes. We draw on social cognitive theory (SCT) to hypothesize and examine how effectively two learning approaches (an observation-only approach and an observation plus hands-on approach) help analysts better understand users’ computer-mediated work processes. Accordingly, we conducted an experimental study to compare these two learning approaches. We found that, while participants who had low prior domain knowledge about users’ work processes ended up understanding them better in the observation plus hands-on treatment than in the observation- only treatment, the difference between the two approaches was not significant for participants who had high prior domain knowledge.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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