Echo: Analyzing Gameplay Sessions by Reconstructing Them From Recorded Data
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
Games user research (GUR) is centered on ensuring games deliver the experience that their designers intended. GUR researchers frequently make use of playtesting to evaluate games. This often requires watching back hours of video footage after the session to ensure that they did not miss anything important. Analytics have been used to help improve this process, providing visualizations of the underlying gameplay data. Yet, many of these game analytics tools provide static visualizations which do not accurately capture the dynamic aspects of modern video games. To address this problem, we have created Echo, a tool that uses gameplay data to reconstruct the original session with in-game assets, instead of abstracting them away. Echo has been designed to help bridge the gap between static gameplay data representation and video footage, with the goal of providing the best of both. A user study revealed that participants found Echo less frustrating to use compared to videos for gameplay analysis and also ranked it higher for efficiency, among others. It revealed that participants felt less cognitive load when using Echo as well. Qualitative results were also promising as participants employed several distinct workflows while using Echo. We received numerous suggestions for building upon the current state of the tool, including support for multiple viewports, live annotations, and visible gameplay metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it