Sally Hamouda, Sahith Kancharla, Gurkirat Singh, Lin Yang, Zhuoqun Wang, Siliang Zhang, Raseen Nirjhar, John Golden
{"title":"KiData: simple data visualization tool for future data scientists","authors":"Sally Hamouda, Sahith Kancharla, Gurkirat Singh, Lin Yang, Zhuoqun Wang, Siliang Zhang, Raseen Nirjhar, John Golden","doi":"10.3389/fcomp.2023.1209515","DOIUrl":null,"url":null,"abstract":"Data and visualizations are powerful tools that provide insights, analysis, and conclusions in a logical and easy-to-understand manner. However, the current school curriculum lacks adequate preparation for students to understand, analyze, interpret, or create complex data visualizations, which can hinder their potential careers in data science. To address this gap, our project aimed to develop a user-friendly web-based tool that provides interactive lessons on data and visualizations for elementary school children. The website consists of 12 lessons, categorized by grade levels (1st–2nd grade, 3rd–4th grade, and 5th–6th grade), and includes an interactive question-answer section. Users can scroll down after reading the lessons and practice questions based on the visualizations. The website also has the potential to incorporate games related to data and visualization. The lessons are implemented using React.js and Java with the Spring framework, and new lessons can easily be added by storing them in a markdown folder. The website features a navigation bar with tabs for Home, Lessons, Games, About, and Contact. Additionally, a feedback form is included to gather user feedback for further improvements. The website is currently in the testing stage, and future surveys for teachers and elementary school students will be added to enhance the features provided. Our study presents preliminary findings and serves as a foundational exploration. We acknowledge that further research and experimentation are required to validate and expand upon the results discussed herein.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fcomp.2023.1209515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Data and visualizations are powerful tools that provide insights, analysis, and conclusions in a logical and easy-to-understand manner. However, the current school curriculum lacks adequate preparation for students to understand, analyze, interpret, or create complex data visualizations, which can hinder their potential careers in data science. To address this gap, our project aimed to develop a user-friendly web-based tool that provides interactive lessons on data and visualizations for elementary school children. The website consists of 12 lessons, categorized by grade levels (1st–2nd grade, 3rd–4th grade, and 5th–6th grade), and includes an interactive question-answer section. Users can scroll down after reading the lessons and practice questions based on the visualizations. The website also has the potential to incorporate games related to data and visualization. The lessons are implemented using React.js and Java with the Spring framework, and new lessons can easily be added by storing them in a markdown folder. The website features a navigation bar with tabs for Home, Lessons, Games, About, and Contact. Additionally, a feedback form is included to gather user feedback for further improvements. The website is currently in the testing stage, and future surveys for teachers and elementary school students will be added to enhance the features provided. Our study presents preliminary findings and serves as a foundational exploration. We acknowledge that further research and experimentation are required to validate and expand upon the results discussed herein.