Sustainable development goals (SDGs) address various aspects of future human development such as poverty, pollution, or climate change. Education for Sustainable Development (ESD) seeks to encourage students to actively participate and consider these issues in sustainable development. Following design-based research approaches, our study aims to identify opportunities for integrating ESD into statistics education. We describe the main features of an ESD-integrated learning trajectory for middle school students exploring sets of ESD related data visually with the software Gapminder. The outcomes of our study suggest that (1) Students’ engagement in forming statistical models of various countries’ sustainable development developed their abilities to generate statistical questions (2) Their intuitive knowledge of statistical concepts was further formalized during their subsequent analyses.
{"title":"Integrating ‘education for sustainable development’ in statistics classes: visual analysis of social and economic data with gapminder","authors":"Martin Andre, Z. Lavicza, Theodosia Prodromou","doi":"10.52041/IASE.20103","DOIUrl":"https://doi.org/10.52041/IASE.20103","url":null,"abstract":"Sustainable development goals (SDGs) address various aspects of future human development such as poverty, pollution, or climate change. Education for Sustainable Development (ESD) seeks to encourage students to actively participate and consider these issues in sustainable development. Following design-based research approaches, our study aims to identify opportunities for integrating ESD into statistics education. We describe the main features of an ESD-integrated learning trajectory for middle school students exploring sets of ESD related data visually with the software Gapminder. The outcomes of our study suggest that (1) Students’ engagement in forming statistical models of various countries’ sustainable development developed their abilities to generate statistical questions (2) Their intuitive knowledge of statistical concepts was further formalized during their subsequent analyses.","PeriodicalId":448781,"journal":{"name":"New Skills in the Changing World of Statistics Education","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Adeboye, Ilaro Federal Polytechnic, Oyedunsi Olayiwola
Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.
{"title":"Big Data Affluence in Statistics Application: A Comparison of Real Life and Simulated Open Data","authors":"N. Adeboye, Ilaro Federal Polytechnic, Oyedunsi Olayiwola","doi":"10.52041/IASE.20102","DOIUrl":"https://doi.org/10.52041/IASE.20102","url":null,"abstract":"Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.","PeriodicalId":448781,"journal":{"name":"New Skills in the Changing World of Statistics Education","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125593674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of statistical literacy/quantitative reasoning has been highlighted for decades; today the need is even more compelling with data science emerging as foundational in many disciplines. Educated students should understand how to make decisions in the presence of uncertainty and how to interpret quantitative information presented to them in the course of their professional and personal activities. Too often, however, students have limited experience in thinking and reasoning based on real data. This paper explores how ideas from data science interface with notions of statistical literacy/quantitative reasoning, considers foundational concepts necessary to enable students to engage with real data sets in the learning process, and identifies potential curricular elements that are important for all students from these perspectives.
{"title":"Statistical literacy and quantitative reasoning: Rethinking the curriculum","authors":"Gail Burrill","doi":"10.52041/IASE.20104","DOIUrl":"https://doi.org/10.52041/IASE.20104","url":null,"abstract":"The importance of statistical literacy/quantitative reasoning has been highlighted for decades; today the need is even more compelling with data science emerging as foundational in many disciplines. Educated students should understand how to make decisions in the presence of uncertainty and how to interpret quantitative information presented to them in the course of their professional and personal activities. Too often, however, students have limited experience in thinking and reasoning based on real data. This paper explores how ideas from data science interface with notions of statistical literacy/quantitative reasoning, considers foundational concepts necessary to enable students to engage with real data sets in the learning process, and identifies potential curricular elements that are important for all students from these perspectives.","PeriodicalId":448781,"journal":{"name":"New Skills in the Changing World of Statistics Education","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125465501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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