Hollylynne S. Lee, G. Mojica, Emily P. Thrasher, Peter Baumgartner
With a call for schools to infuse data across the curriculum, many are creating curricula and examining students’ thinking in data-intensive problems. As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists. Together, these inform a new framework to support data investigation processes. We explicate the practices and dispositions needed and offer a glimpse of how the framework can be used to move the discipline of data science education forward.
{"title":"INVESTIGATING DATA LIKE A DATA SCIENTIST: KEY PRACTICES AND PROCESSES","authors":"Hollylynne S. Lee, G. Mojica, Emily P. Thrasher, Peter Baumgartner","doi":"10.52041/serj.v21i2.41","DOIUrl":"https://doi.org/10.52041/serj.v21i2.41","url":null,"abstract":"With a call for schools to infuse data across the curriculum, many are creating curricula and examining students’ thinking in data-intensive problems. As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists. Together, these inform a new framework to support data investigation processes. We explicate the practices and dispositions needed and offer a glimpse of how the framework can be used to move the discipline of data science education forward. ","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47674894","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}
Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.
{"title":"WORK INTEGRATED LEARNING IN STATISTICS AND COMPUTER SCIENCE AND FAIR ASSESSMENT OF AUTHENTIC PROJECTS","authors":"A. Bilgin, Angela M. Powell, Deborah Richards","doi":"10.52041/serj.v21i2.26","DOIUrl":"https://doi.org/10.52041/serj.v21i2.26","url":null,"abstract":"Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49073933","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}
This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.
{"title":"TEACHING AND LEARNING DATA-DRIVEN MACHINE LEARNING WITH EDUCATIONALLY DESIGNED JUPYTER NOTEBOOKS","authors":"Yannik Fleischer, Rolf Biehler, Carsten Schulte","doi":"10.52041/serj.v21i2.61","DOIUrl":"https://doi.org/10.52041/serj.v21i2.61","url":null,"abstract":"This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41678131","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}
Many important voices--including The National Council for Teachers of Mathematics (NCTM), the Dana Center’s Launch Years initiative, and others--advocate for expanding the traditional course offerings in high school mathematics and statistics to include courses such as the Introduction to Data Science (IDS). To date, the research on the IDS course has primarily focused on pedagogy, professional learning for teachers, and the curriculum. This mixed-methods case study expands our understanding by analyzing the perspective of IDS students at a California public high school. Self-determination theory provides a useful frame for interpreting how these students experience the IDS course. The theory focuses on conditions for students to engage in meaningful learning: competence (self-efficacy), autonomy (agency), and relatedness (a sense of belonging). The findings from this case study suggest the IDS students feel confident, empowered, and part of a vibrant community, unlike previous mathematics and statistics courses they may have completed; and use specific language to describe their joy in problem-solving and the accessibility of the course. These findings have implications for the development and refinement of any high school data science course, including IDS.
{"title":"“I LOVE MATH ONLY IF IT'S CODING”: A CASE STUDY OF STUDENT EXPERIENCES IN AN INTRODUCTION TO DATA SCIENCE COURSE","authors":"Erica Heinzman","doi":"10.52041/serj.v21i2.43","DOIUrl":"https://doi.org/10.52041/serj.v21i2.43","url":null,"abstract":"Many important voices--including The National Council for Teachers of Mathematics (NCTM), the Dana Center’s Launch Years initiative, and others--advocate for expanding the traditional course offerings in high school mathematics and statistics to include courses such as the Introduction to Data Science (IDS). To date, the research on the IDS course has primarily focused on pedagogy, professional learning for teachers, and the curriculum. This mixed-methods case study expands our understanding by analyzing the perspective of IDS students at a California public high school. Self-determination theory provides a useful frame for interpreting how these students experience the IDS course. The theory focuses on conditions for students to engage in meaningful learning: competence (self-efficacy), autonomy (agency), and relatedness (a sense of belonging). The findings from this case study suggest the IDS students feel confident, empowered, and part of a vibrant community, unlike previous mathematics and statistics courses they may have completed; and use specific language to describe their joy in problem-solving and the accessibility of the course. These findings have implications for the development and refinement of any high school data science course, including IDS.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47574987","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}
First published February 2020 at Statistics Education Research Journal Archives
2020年2月首次发表于《统计教育研究期刊档案》
{"title":"NOTE FROM THE EDITOR FOR REGULAR PAPERS","authors":"Jennifer J. Kaplan","doi":"10.52041/serj.v19i1.589","DOIUrl":"https://doi.org/10.52041/serj.v19i1.589","url":null,"abstract":"First published February 2020 at Statistics Education Research Journal Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44696055","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}
First published February 2020 at Statistics Education Research Journal Archives
首次发表于2020年2月的《统计教育研究期刊档案》
{"title":"EDITORIAL FROM THE SPECIAL-ISSUES CO-EDITOR OF SERJ","authors":"M. Borovcnik","doi":"10.52041/serj.v19i1.590","DOIUrl":"https://doi.org/10.52041/serj.v19i1.590","url":null,"abstract":"First published February 2020 at Statistics Education Research Journal Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42574992","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}
First published May 2009 at Statistics Education Research Journal: Archives
首次发表于2009年5月的《统计教育研究杂志:档案》
{"title":"Editorial and Front Matter","authors":"Tom Short","doi":"10.52041/serj.v8i1.454","DOIUrl":"https://doi.org/10.52041/serj.v8i1.454","url":null,"abstract":"First published May 2009 at Statistics Education Research Journal: Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42612345","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 Survey of Attitudes Toward Statistics (SATS) is a widely used family of instruments for measuring attitude constructs in statistics education. Since the development of the SATS instruments, there has been an evolution in the understanding of validity in the field of educational measurement emphasizing validation as an on-going process. While a 2012 review of statistics education attitude instruments noted that the SATS family had the most validity evidence, two types of challenges to the use of these instruments have emerged: challenges to the interpretations of scale scores and challenges using the SATS instruments in populations other than undergraduate students enrolled in introductory statistics courses. A synthesis of the literature and empirical results are used to document these challenges.
{"title":"CHALLENGES ASSOCIATED WITH MEASURING ATTITUDES USING THE SATS FAMILY OF INSTRUMENTS","authors":"Douglas Whitaker, A. Unfried, Marjorie E. Bond","doi":"10.52041/serj.v21i1.88","DOIUrl":"https://doi.org/10.52041/serj.v21i1.88","url":null,"abstract":"The Survey of Attitudes Toward Statistics (SATS) is a widely used family of instruments for measuring attitude constructs in statistics education. Since the development of the SATS instruments, there has been an evolution in the understanding of validity in the field of educational measurement emphasizing validation as an on-going process. While a 2012 review of statistics education attitude instruments noted that the SATS family had the most validity evidence, two types of challenges to the use of these instruments have emerged: challenges to the interpretations of scale scores and challenges using the SATS instruments in populations other than undergraduate students enrolled in introductory statistics courses. A synthesis of the literature and empirical results are used to document these challenges.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48510830","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}
Statistical literacy is a skill that will be required by all students, but only limited insights exist into how it can be developed in middle schools. Research is required that identifies design principles and provides didactic materials for developing statistical literacy in actual middle school classrooms, meaning classrooms in which statistics is only seen as a small part of mathematics. This study conceptualizes statistical literacy as not only the ability to read given statistical information, but also as the ability to imagine the often unreported data and underlying assumptions of this information. This allows the Design Research study to identify design principles for developing statistical literacy in which students actively engage with conflicting statistical information about the same data. The working mechanisms of the design principles are illustrated through didactic materials, and student responses show how the design principles can be used to develop statistical literacy in middle schools.
{"title":"DESIGN PRINCIPLES FOR DEVELOPING STATISTICAL LITERACY IN MIDDLE SCHOOLS","authors":"C. Büscher","doi":"10.52041/serj.v21i1.80","DOIUrl":"https://doi.org/10.52041/serj.v21i1.80","url":null,"abstract":"\u0000Statistical literacy is a skill that will be required by all students, but only limited insights exist into how it can be developed in middle schools. Research is required that identifies design principles and provides didactic materials for developing statistical literacy in actual middle school classrooms, meaning classrooms in which statistics is only seen as a small part of mathematics. This study conceptualizes statistical literacy as not only the ability to read given statistical information, but also as the ability to imagine the often unreported data and underlying assumptions of this information. This allows the Design Research study to identify design principles for developing statistical literacy in which students actively engage with conflicting statistical information about the same data. The working mechanisms of the design principles are illustrated through didactic materials, and student responses show how the design principles can be used to develop statistical literacy in middle schools. \u0000","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48167703","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}
Recent approaches to statistics education situate the teaching and learning of statistics within cycles of statistical inquiry. Learners pose questions, plan, and collect, represent, analyse and interpret data. We focus on the first step – posing statistical questions. Posing statistical questions is a critical step as questions inform the types of data collected, determine the representations used, and influence the interpretations that can be made. We report on an investigation of 158 prospective elementary teachers as they design statistical questions to support group comparisons. Support was provided through implementation of three phases of question development (think, peer-feedback, and expert-feedback). We describe the features of initial statistical questions posed, examine refinements made to statistical questions, and evaluate the effectiveness of both peer and expert feedback. Our study reveals that generating adequate statistical questions is particularly complex and requires considerable time, targeted feedback, and support. With appropriate support, in the form of peer and expert feedback provided within a three-phase question design scenario, prospective elementary teachers could generate adequate statistical questions suitable for use in primary classrooms. While this study provides compelling evidence to support the use of expert feedback, further research is required to identify the best ways to support prospective teachers in both providing and implementing peer-feedback.
{"title":"DEVELOPING THE STATISTICAL PROBLEM POSING AND PROBLEM REFINING SKILLS OF PROSPECTIVE TEACHERS","authors":"A. Leavy, Daniel Frischemeier","doi":"10.52041/serj.v21i1.226","DOIUrl":"https://doi.org/10.52041/serj.v21i1.226","url":null,"abstract":"\u0000Recent approaches to statistics education situate the teaching and learning of statistics within cycles of statistical inquiry. Learners pose questions, plan, and collect, represent, analyse and interpret data. We focus on the first step – posing statistical questions. Posing statistical questions is a critical step as questions inform the types of data collected, determine the representations used, and influence the interpretations that can be made. We report on an investigation of 158 prospective elementary teachers as they design statistical questions to support group comparisons. Support was provided through implementation of three phases of question development (think, peer-feedback, and expert-feedback). We describe the features of initial statistical questions posed, examine refinements made to statistical questions, and evaluate the effectiveness of both peer and expert feedback. Our study reveals that generating adequate statistical questions is particularly complex and requires considerable time, targeted feedback, and support. With appropriate support, in the form of peer and expert feedback provided within a three-phase question design scenario, prospective elementary teachers could generate adequate statistical questions suitable for use in primary classrooms. While this study provides compelling evidence to support the use of expert feedback, further research is required to identify the best ways to support prospective teachers in both providing and implementing peer-feedback.\u0000","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45847498","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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