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

Xin Wei, Susu Zhang, Jihong Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1423602
JournalFrontiers in Education
Volume9
DOIs
StatePublished - 2024

Keywords

  • cluster analysis
  • cognitive load
  • mental rotation
  • NAEP
  • process data
  • student profiles
  • universal design tools
  • visualization strategies

ASJC Scopus subject areas

  • Education

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