Identifying student profiles in a digital mental rotation task: insights from the 2017 NAEP math assessment

Mental rotation (MR), a key aspect of spatial reasoning, is highly predictive of success in STEM fields. This study analyzed strategies employed by 27,600 eighth-grade students during a digital MR task from the 2017 National Assessment of Educational Progress (NAEP) in mathematics. Utilizing K-means cluster analysis to categorize behavioral and performance patterns, we identified four distinct profiles: Cognitive Offloaders (15% of the sample), Internal Visualizers (55%), External Visualizers (5%), and Non-Triers (25%). Cognitive Offloaders, skilled at minimizing cognitive load by eliminating incorrect options, demonstrated the highest MR accuracy rates at 45%. Internal Visualizers, relying less on digital tools and more on mental strategies, achieved robust performance with an average score of 38%. External Visualizers, despite their extensive use of assistive tools and greater time investment, scored an average of 36%. Non-Triers showed minimal engagement and correspondingly the lowest performance, averaging 29%. These findings not only underscore the diverse strategies students adopt in solving MR tasks but also emphasize the need for educational strategies that are tailored to accommodate different cognitive styles. By integrating MR training into the curriculum and enhancing teacher preparedness to support diverse learning needs, this study advocates for educational reforms to promote equitable outcomes in mathematics and broader STEM fields.

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Publication Details

Journal
Frontiers in Education
Published
2024-08-05
DOI
https://doi.org/10.3389/feduc.2024.1423602
Citations
1
Primary Topic
Spatial Cognition and Navigation
Type
article
Field-Weighted Citation Impact
0.20
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article

Identifying student profiles in a digital mental rotation task: insights from the 2017 NAEP math assessment

Xin Wei, Susu Zhang, Jihong Zhang
1 citations
Frontiers in Education
Spatial Cognition and Navigation
0.20
article

Identifying student profiles in a digital mental rotation task: insights from the 2017 NAEP math assessment

Xin Wei, Susu Zhang, Jihong Zhang
article en
1 citations

Abstract

Mental rotation (MR), a key aspect of spatial reasoning, is highly predictive of success in STEM fields. This study analyzed strategies employed by 27,600 eighth-grade students during a digital MR task from the 2017 National Assessment of Educational Progress (NAEP) in mathematics. Utilizing K-means cluster analysis to categorize behavioral and performance patterns, we identified four distinct profiles: Cognitive Offloaders (15% of the sample), Internal Visualizers (55%), External Visualizers (5%), and Non-Triers (25%). Cognitive Offloaders, skilled at minimizing cognitive load by eliminating incorrect options, demonstrated the highest MR accuracy rates at 45%. Internal Visualizers, relying less on digital tools and more on mental strategies, achieved robust performance with an average score of 38%. External Visualizers, despite their extensive use of assistive tools and greater time investment, scored an average of 36%. Non-Triers showed minimal engagement and correspondingly the lowest performance, averaging 29%. These findings not only underscore the diverse strategies students adopt in solving MR tasks but also emphasize the need for educational strategies that are tailored to accommodate different cognitive styles. By integrating MR training into the curriculum and enhancing teacher preparedness to support diverse learning needs, this study advocates for educational reforms to promote equitable outcomes in mathematics and broader STEM fields.

Frontiers in EducationVol. 9
University of Illinois Urbana-Champaign (US), Digital Promise (US), University of Arkansas at Fayetteville (US)
Quality Education
Openalex Percentile: Top 52%
Spatial Cognition and Navigation
0.20
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Identifying student profiles in a digital mental rotation task: insights from the 2017 NAEP math assessment — Xin Wei, Susu Zhang, et al. · Frontiers in Education (2024) | TGRS Research Map | TGRS