TY - JOUR
T1 - Identifying student profiles in a digital mental rotation task
T2 - insights from the 2017 NAEP math assessment
AU - Wei, Xin
AU - Zhang, Susu
AU - Zhang, Jihong
N1 - Publisher Copyright:
Copyright © 2024 Wei, Zhang and Zhang.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - cluster analysis
KW - cognitive load
KW - mental rotation
KW - NAEP
KW - process data
KW - student profiles
KW - universal design tools
KW - visualization strategies
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U2 - 10.3389/feduc.2024.1423602
DO - 10.3389/feduc.2024.1423602
M3 - Article
AN - SCOPUS:85201394734
SN - 2504-284X
VL - 9
JO - Frontiers in Education
JF - Frontiers in Education
M1 - 1423602
ER -