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

Qiwei He, Yiming Chen, Elizabeth L Tighe, Gal Kaldes

Abstract read

In one paragraph

Article in Journal of Intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Qiwei HeDepartment of Psychology and Data Science and Analytics Program, Georgetown University, Washington, DC 20007, USA.ORCID 0000-0001-8942-2047

Yiming ChenDepartment of Educational Psychology, University of Minnesota, Twin Cities, MN 55455, USA.

Elizabeth L TigheDepartment of Psychology, Georgia State University, Atlanta, GA 30303, USA.

Gal KaldesAdult Literacy Research Center, Department of Learning Sciences, Georgia State University, Atlanta, GA 30303, USA.

Funding

Institute for Education Science R305A210344

6 · The paper itself

Abstract

Technological advances have transformed the landscape of educational assessments, particularly in how we collect and analyze data on individuals' cognitive processes and interactions with assessment items. The rich data recorded in log files during human-machine interactions are often referred to as process data. This study uses sequential process data from the 2012 Program for the International Assessment of Adult Competencies (PIAAC), focusing specifically on how respondents navigate an interactive numeracy item ("Map") that involves spatial cognition. The objectives of this study are trifold: (1) to explore factors that contribute to success or failure on a spatial numeracy item, (2) to identify spatial cognitive process features across high and low numeracy performance levels, and (3) to introduce a novel time-embedded

Indexed as

educational assessmentmachine learningnumeracyPIAACspatial cognitive processestime-embedded n-grams

Identifiers

PMID42346720

DOI10.3390/jintelligence14060105

PMCPMC13301755

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.