[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-586155-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-586155-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","exploring-the-integration-of-data-visualization-through-self-quantification-insights-from-school-education","Exploring the Integration of Data Visualisation Through Self-Quantification - Insights From School Education","","Exploring the integration of data visualisation through self-quantification, this literature review reframes data visualisation within school education and proposes a guiding framework. It connects big-data-driven tracking tools and self-quantification to mathematics education, emphasizing dynamic visual displays that support interpretation, change tracking, and informed decisions. The paper identifies curriculum and teacher-capability gaps that complicate interdisciplinary data science integration and formulates two research questions to clarify definitions and learning activities.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/exploring-the-integration-of-data-visualization-through-self-quantification-insights-from-school-education/586155/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/exploring-the-integration-of-data-visualization-through-self-quantification-insights-from-school-education/586155.png","ImageObject",300,407,{"name":42,"@type":43},"Taylor Morgan","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What does the paper aim to do regarding data visualisation and self-quantification?","Question",{"text":62,"@type":63},"It reconceptualises data visualisation through the lens of school education and proposes a framework. It also examines how data visualisation can be utilised within activities framed by self-quantification.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How does the paper describe self-quantification in relation to big data?",{"text":67,"@type":63},"Self-quantification is presented as a process enabled by wearable devices and mobile apps that collect and analyse real-time physical and emotional data. Visualisation helps users interpret their data and make informed decisions.",{"name":69,"@type":60,"acceptedAnswer":70},"Why is integrating data science and data visualisation into school curricula challenging?",{"text":71,"@type":63},"The paper points to interdisciplinary demands spanning mathematics, computer science, and statistics, plus limited curricular detail on effective visualisation for sense-making. 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This literature review reconceptualises data visualisation through the lens of school education and proposes a framework. By framing data visualisation as both a product and a process, the paper explores how data visualisation be utilised in activities framed within the context of self-quantification.  \nBackground  \nBig data, which has been re-shaping modern lives through the way we think and act, significantly expands the amount of information available for use in various aspects of life, enabling innovative ways through which people engage in data across different fields (Cukier & Mayer-Schoenberger, 2013) . One such innovation is the development of wearable devices and mobile apps that track individuals’ physical and emotional activities. By collecting and analysing real-time data, these technologies offer valuable insights into health, behaviour, and well-being, illustrating the potential of big data to transform everyday experiences into quantifiable metrics -a process known as self-quantification (Maltseva & Lutz, 2018) . Selfquantification relies on data visualisation, which enables users to interpret their data, track changes, and make informed decisions through real-time visual displays. From the perspective of mathematics education, this process of self-quantification provides an engaging and authentic context for students to interact with real-time and personal and dynamic data via dynamic data visualisations.  \nGiven the potential of self-quantification and data visualisation to enhance students’engagement with real-world data, integrating data science projects grounded in the context of self-quantification emerges as a valuable endeavour (Lee, 2019) . However, integrating innovative data science projects into an already overcrowded curriculum can be challenging for many school educators. One possible reason is that data science draws on content knowledge from multiple disciplines, including mathematics, computer science, and statistics, as well as domain-specific knowledge of different subjects (Donoho, 2017) . The interdisciplinary nature of data science makes it complex to be integrated into one single subject area without collaboration between educators from different disciplines (Herro et al., 2022) . Additionally, curricula often lack sufficient detail on how data can be effectively visualised as a tool for students to make sense of in data-rich investigations (Australian Curriculum and Reporting Authority [ACARA], 2022; Ministry of Education Singapore [MOE], 2021) . This gap in curricular guidance contributes to challenges in equipping teachers with the necessary skills and confidence to integrate data visualisation into their teaching practices. A recent study on over 3,000 primary and secondary teachers’ perceptions of teaching data science indicate that many teachers do not feel confident in using data visualisation as a tool to support students’learning due to their lack of content knowledge and experience in data science (Filderman et al., 2022) . To begin to address the problems discussed above, this paper aims to investigate following research questions:  \nRQ1 – How has the literature defined data visualisation relevant to school education?  \nRQ2-How can data visualisation be utilised in activities framed within the context of selfquantification?  \nThe paper is structured in two main parts. The first part presents a literature review that explores data visualisation within the cont","cbCaig7VEJpAFREr","https://ap.wps.com/l/cbCaig7VEJpAFREr","pdf",443946,"English","# Background\n# Literature Review\n## Data Visualisation within the Context of School Mathematics Curriculum","[{\"question\":\"What does the paper aim to do regarding data visualisation and self-quantification?\",\"answer\":\"It reconceptualises data visualisation through the lens of school education and proposes a framework. It also examines how data visualisation can be utilised within activities framed by self-quantification.\"},{\"question\":\"How does the paper describe self-quantification in relation to big data?\",\"answer\":\"Self-quantification is presented as a process enabled by wearable devices and mobile apps that collect and analyse real-time physical and emotional data. Visualisation helps users interpret their data and make informed decisions.\"},{\"question\":\"Why is integrating data science and data visualisation into school curricula challenging?\",\"answer\":\"The paper points to interdisciplinary demands spanning mathematics, computer science, and statistics, plus limited curricular detail on effective visualisation for sense-making. It also highlights teachers’ lack of confidence due to insufficient content knowledge and experience.\"}]","Exploring the Integration of Data Visualisation Through Self-Quantification - Insights From School Education | PDF",1792057101]