The way it made me feel – Creating and evaluating an in-app feedback tool for mobile apps
Bibliographic record
Abstract
Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool.
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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 | 0.029 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".