[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-42821-en":3,"doc-seo-42821-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},42821,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Spatial visualization and measurement of area: A case spatialized mathematics instruction","The study explores how spatial visualization skills influence students’ success on area tasks. Spatial visualization links closely to mathematics achievement, yet the mechanisms connecting these skills to task performance are insufficiently understood. Middle school students’ representations and solutions to both non-metric and metric area problems were analyzed using qualitative and quantitative task analysis. Results indicate strong spatial visualizers solve tasks with conceptual and procedural understanding, while Low and Average students show more errors from incorrect linear measurement or formula use, despite procedural knowledge. Spatial skills also facilitate the relationship between metric task representations and solution success.","Journal of Mathematical Behavior 70 (2023) 101038  \nContents lists available at ScienceDirect  \nJournal of Mathematical Behavior  \njournal [homepage: www.elsevier.com/locate/jmathb](homepage: www.elsevier.com/locate/jmathb)  \n| Spatial visualization and measurement of area: A case spatialized mathematics instruction☆\u003Cbr>*\u003Cbr>Danielle Harris , Tracy Logan, Tom Lowrie\u003Cbr>University of Canberra, Faculty of Education, Canberra, Australia |  |  | study in |  |\n| --- | --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |  |\n| Keywords:\u003Cbr>Spatial visualization Measurement\u003Cbr>Area\u003Cbr>Learning pathways |  | The purpose of this study was to explore the influence of spatial visualization skills when students solve area tasks. Spatial visualization is closely related to mathematics achievement, but little is known about how these skills link to task success. We examined middle school students’ representations and solutions to area problems (both non-metric and metric) through qualitative and quantitative task analysis. Task solutions were analyzed as a function of spatial visualization skills and links were made between student solutions on tasks with different goals (i.e., non-metric and metric). Findings suggest that strong spatial visualizers solved the tasks with relative ease, with evidence for conceptual and procedural understanding. By contrast, Low and Average Spatial students more frequently produced errors due to failure to correctly determine linear measurements or apply appropriate formula, despite adequate procedural knowledge. A novel finding was the facilitating role of spatial skills in the link between metric task representation and success in determining a solution. From a teaching and learning perspective, these results highlight the need to connect emergent spatial skills with mathematical content and support students to develop conceptual understanding in parallel with procedural competence. |  |  |\n\n1. Introduction  \nStrong spatial skills are considered a critical precursor to mathematics proficiency (Hegarty & Kozhevnikov, 1999), so much so that academic literature is no longer focused on whether the two are related, but how (Mix, 2019; Mix & Cheng, 2012). Studies into the impact of spatial training on mathematics outcomes are showing promise (Gilligan-Lee et al., 2020; Hawes et al., 2022; Mix et al., 2021; Lowrie et al., 2021), however the nature of the mechanisms that enable transfer between skills training and mathematics performance remain theoretical (Hawes & Ansari, 2020; Lowrie et al., 2020). This study attempts to deepen our understanding of mathematical-spatial relations by exploring how students engage with spatialized area instruction through analysis of task representation and success, and how this relates to their spatial visualization skills.  \n1.1. Focusing in on geometry  \nIt has been argued that the diagrammatic representations found in geometry tasks have spatial elements (Bishop, 1980), yet the  \n☆ The dataset generated during the current study is not publicly available as it contains confidential information related to the study participants. Information on how to obtain access to the data is available from the corresponding author on request.  \n* Correspondence to: University Drive, Bruce, ACT, 2617, Australia.  \nE-mail address: [danielle.harris@canberra.edu.au](danielle.harris@canberra.edu.au) (D. Harris).  \n[https://doi.org/10.1016/j.jmathb.2023.101038](https://doi.org/10.1016/j.jmathb.2023.101038)  \nReceived 11 May 2022; Received in revised form 2 February 2023; Accepted 5 February 2023 Available online 16 February 2023  \n0732-3123/© 2023 Elsevier Inc. All rights reserved.  \ncontent includes mathematical language and conventions not supported by spatial processes alone (Hawes et al., 2022; Mix & Cheng, 2012; Newcombe, 2018). Mathematics education researchers maintain that geometric concepts are inherently spatial and therefore are frequently the part of mathematics curri","cbCaifZNCJrJylnA","https://ap.wps.com/l/cbCaifZNCJrJylnA","pdf",3409374,4,1,18,"English","en",105,"# Introduction\n## Focusing in on geometry","[{\"question\":\"What was the purpose of the study on area tasks?\",\"answer\":\"The study examined how spatial visualization skills affect students’ ability to solve area tasks and how those skills relate to task success.\"},{\"question\":\"How did the researchers analyze student performance?\",\"answer\":\"They used qualitative and quantitative task analysis to examine middle school students’ representations and solutions for both non-metric and metric area problems.\"},{\"question\":\"What differences were observed between strong spatial visualizers and Low/Average students?\",\"answer\":\"Strong spatial visualizers solved tasks with relative ease, showing evidence of conceptual and procedural 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