ExperienceSampler: An Open-Source Scaffold for Building Experience Sampling Smartphone Apps
Bibliographic record
Abstract
Experience sampling methods allow researchers to examine phenomena in daily life and provide various advantages that complement traditional laboratory methods. However, existing experience sampling methods may be costly, require constant Internet connectivity, may not be designed specifically for experience sampling studies, or require a custom solution from a computer programming consultant. In this paper, we present ExperienceSampler, an open-source scaffold for creating experience-sampling smartphone apps designed for Android and iOS devices. We designed ExperienceSampler to address the common barriers to using experience sampling methods. First, there is no cost to the user. Second, ExperienceSampler apps make use of local notifications to let participants know when to complete surveys and store the data locally until Internet connection is available. Third, our app scaffold was designed with experience sampling methodological issues in mind. We also demonstrate how researchers can easily customize ExperienceSampler even if they have no programming skills. Furthermore, we evaluate the utility of ExperienceSampler apps with results from one social psychological study conducted using ExperienceSampler (N = 168). Mean response rates averaged 84%, and the median response latency was 10 minutes. Taken together, ExperienceSampler creates cost-effective smartphone apps that can be easily customized by researchers to examine experiences in daily life.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".