Understanding Adults’ Spatial Cognitive Processes: A Time-Embedded N-Grams Model with Machine Learning
Qiwei He, Yiming Chen, Elizabeth L. Tighe, Gal Kaldes · 2026
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Description
How adults fumble through a map task leaves digital footprints that reveal their numeracy, Qiwei He and colleagues demonstrate in a 2026 study. Using log data from the 2012 PIAAC survey, they followed 596 United States adults working an interactive map item. A new time-embedded n-grams model folded elapsed time into action sequences, feeding random forest and XGBoost predictors.
Time-related features topped the predictor list, joined by how well respondents handled directions in space. The models separated high from low performers robustly, showing that the path taken, not just the answer given, carries skill signals. The authors pitch process data as a richer lens for testing programs and cognitive research.
Catalog record
| CREATOR | Qiwei He, Yiming Chen, Elizabeth L. Tighe, Gal Kaldes |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66085 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |