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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Related Experiment Video

Updated: Jun 26, 2026

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Understanding Adults' Spatial Cognitive Processes: A Time-Embedded N-Grams Model with Machine Learning.

Qiwei He1, Yiming Chen2, Elizabeth L Tighe3,4

  • 1Department of Psychology and Data Science and Analytics Program, Georgetown University, Washington, DC 20007, USA.

Journal of Intelligence
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

Analyzing adult numeracy skills using process data from the Program for the International Assessment of Adult Competencies (PIAAC) revealed that time and spatial understanding are key predictors of success. This highlights the value of detailed interaction data in educational assessments.

Keywords:
PIAACeducational assessmentmachine learningnumeracyspatial cognitive processestime-embedded n-grams

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Last Updated: Jun 26, 2026

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

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Published on: May 7, 2014

Area of Science:

  • Educational Measurement
  • Cognitive Science
  • Data Science

Background:

  • Technological advancements enable detailed collection and analysis of process data from educational assessments.
  • Process data, capturing human-machine interactions, offers insights into cognitive processes during assessments.

Purpose of the Study:

  • To explore factors influencing success/failure on spatial numeracy items.
  • To identify spatial cognitive process features associated with different numeracy performance levels.
  • To introduce a novel time-embedded n-grams model for analyzing sequential process data.

Main Methods:

  • Utilized sequential process data from the 2012 Program for the International Assessment of Adult Competencies (PIAAC).
  • Employed a time-embedded n-grams model, random forest, and XGBoost machine learning methods.
  • Analyzed data from 596 U.S. adult respondents interacting with a spatial numeracy item.

Main Results:

  • Time-related features and understanding directions in spatial dimensions were most predictive of numeracy skills.
  • Machine learning models successfully predicted respondents' numeracy performance.
  • Identified robust classifiers for distinguishing high and low numeracy performance levels.

Conclusions:

  • Process data holds significant potential for analyzing latent numeracy skills and cognitive processes.
  • The time-embedded n-grams model effectively incorporates temporal information into sequential action analysis.
  • Findings underscore the importance of temporal and spatial cognitive factors in adult numeracy.