Pub Date : 2024-01-05DOI: 10.1080/26939169.2024.2302179
Christian Genest, James A. Hanley, Sahir R. Bhatnagar
{"title":"Investigating Sensitive Issues in Class Through Randomized Response Polling","authors":"Christian Genest, James A. Hanley, Sahir R. Bhatnagar","doi":"10.1080/26939169.2024.2302179","DOIUrl":"https://doi.org/10.1080/26939169.2024.2302179","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"75 22","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381496","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}
Pub Date : 2024-01-05DOI: 10.1080/26939169.2024.2302185
Lu Ye, Yu Jin
{"title":"Teaching Students to Read COVID-19 Journal Articles in Statistics Courses","authors":"Lu Ye, Yu Jin","doi":"10.1080/26939169.2024.2302185","DOIUrl":"https://doi.org/10.1080/26939169.2024.2302185","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"43 39","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382407","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}
Pub Date : 2024-01-02DOI: 10.1080/26939169.2024.2296266
{"title":"Journal of Statistics and Data Science Education 2023 Associate Editors","authors":"","doi":"10.1080/26939169.2024.2296266","DOIUrl":"https://doi.org/10.1080/26939169.2024.2296266","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"12 4","pages":"i - i"},"PeriodicalIF":1.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139124770","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}
Pub Date : 2024-01-02DOI: 10.1080/26939169.2024.2293393
Nicholas Horton
{"title":"Interviews of Notable Statistics and Data Science Educators","authors":"Nicholas Horton","doi":"10.1080/26939169.2024.2293393","DOIUrl":"https://doi.org/10.1080/26939169.2024.2293393","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"8 8","pages":"1 - 2"},"PeriodicalIF":1.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139124813","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}
Pub Date : 2023-11-03DOI: 10.1080/26939169.2023.2277847
Allison S. Theobold, Megan H. Wickstrom, Stacey A. Hancock
– Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this paper we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model (Schulte, 2008) to frame our analysis, we explore two types of research questions which could be posed about students’ learning.
{"title":"Coding Code: Qualitative Methods for Investigating Data Science Skills","authors":"Allison S. Theobold, Megan H. Wickstrom, Stacey A. Hancock","doi":"10.1080/26939169.2023.2277847","DOIUrl":"https://doi.org/10.1080/26939169.2023.2277847","url":null,"abstract":"– Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this paper we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model (Schulte, 2008) to frame our analysis, we explore two types of research questions which could be posed about students’ learning.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 38","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818592","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}
Pub Date : 2023-10-31DOI: 10.1080/26939169.2023.2276844
Alexis Lerner, Andrew Gelman
Data literacy for students in non-quantitative fields is important as statistics become the grammar of research and how the world’s decisions are made. Statistics courses are typically offered by mathematics or statistics departments or by social and natural sciences such as economics, political science, psychology, and biology. Here we discuss how to construct a statistics course for students in non-quantitative fields, with a goal of integrating statistical material with students' substantive interests, using student-focused teaching methods and technology to increase student involvement. We demonstrate this kind of hybrid course with the example of an introductory applied statistics class, taught at both the University of Toronto's Anne Tanenbaum Centre for Jewish Studies and the United States Naval Academy.
{"title":"IN PURSUIT OF CAMPUS-WIDE DATA LITERACY: A GUIDE TO DEVELOPING A STATISTICS COURSE FOR STUDENTS IN NON-QUANTITATIVE FIELDS","authors":"Alexis Lerner, Andrew Gelman","doi":"10.1080/26939169.2023.2276844","DOIUrl":"https://doi.org/10.1080/26939169.2023.2276844","url":null,"abstract":"Data literacy for students in non-quantitative fields is important as statistics become the grammar of research and how the world’s decisions are made. Statistics courses are typically offered by mathematics or statistics departments or by social and natural sciences such as economics, political science, psychology, and biology. Here we discuss how to construct a statistics course for students in non-quantitative fields, with a goal of integrating statistical material with students' substantive interests, using student-focused teaching methods and technology to increase student involvement. We demonstrate this kind of hybrid course with the example of an introductory applied statistics class, taught at both the University of Toronto's Anne Tanenbaum Centre for Jewish Studies and the United States Naval Academy.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"36 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135808696","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}
Pub Date : 2023-10-27DOI: 10.1080/26939169.2023.2276445
Jeff Witmer
Data reported from memory can be unreliable. A simple activity lets students experience this firsthand.
从内存中报告的数据可能不可靠。一个简单的活动可以让学生亲身体验。
{"title":"Can you trust your memory?","authors":"Jeff Witmer","doi":"10.1080/26939169.2023.2276445","DOIUrl":"https://doi.org/10.1080/26939169.2023.2276445","url":null,"abstract":"Data reported from memory can be unreliable. A simple activity lets students experience this firsthand.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"7 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234201","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}
Pub Date : 2023-10-27DOI: 10.1080/26939169.2023.2276441
Dennis Tay
Data analytics and programming skills are increasingly important in the humanities, especially in disciplines like linguistics due to the rapid growth of natural language processing (NLP) technologies. However, attitudes and perceptions of students as novice learners, and the attendant pedagogical implications, remain underexplored. This paper reports a combined SWOT (strengths, weaknesses, opportunities, threats) and survey analysis of how postgraduate linguistics students reflect on internal qualities and external circumstances that affect their learning. SWOT is a popular self-reflective strategic planning tool by organizations. An innovative approach was used to classify students into four SWOT-defined learner dispositions (SO, ST, WO, and WT) based on their relative emphasis on strengths vs. weaknesses, and opportunities vs. threats. Scores on a modified Mathematics Attitude Survey measuring self-rated ABILITY, INTEREST, UTILITY, and PERSONAL GROWTH were then compared across these dispositions. Results reveal i) some unexpected and interesting strengths/weaknesses/opportunities/threats, ii) perceived internal traits (strengths/weaknesses) play a greater role than external traits (opportunities/threats) in shaping students’ attitudes, iii) a paradox where more confident students tend to be less interested, and vice-versa. Pedagogical implications arising from the results are discussed with an eye on enhancing the teaching of data analytics and programming skills to this target population.
{"title":"Data analytics and programming for linguistics students: A SWOT and survey study","authors":"Dennis Tay","doi":"10.1080/26939169.2023.2276441","DOIUrl":"https://doi.org/10.1080/26939169.2023.2276441","url":null,"abstract":"Data analytics and programming skills are increasingly important in the humanities, especially in disciplines like linguistics due to the rapid growth of natural language processing (NLP) technologies. However, attitudes and perceptions of students as novice learners, and the attendant pedagogical implications, remain underexplored. This paper reports a combined SWOT (strengths, weaknesses, opportunities, threats) and survey analysis of how postgraduate linguistics students reflect on internal qualities and external circumstances that affect their learning. SWOT is a popular self-reflective strategic planning tool by organizations. An innovative approach was used to classify students into four SWOT-defined learner dispositions (SO, ST, WO, and WT) based on their relative emphasis on strengths vs. weaknesses, and opportunities vs. threats. Scores on a modified Mathematics Attitude Survey measuring self-rated ABILITY, INTEREST, UTILITY, and PERSONAL GROWTH were then compared across these dispositions. Results reveal i) some unexpected and interesting strengths/weaknesses/opportunities/threats, ii) perceived internal traits (strengths/weaknesses) play a greater role than external traits (opportunities/threats) in shaping students’ attitudes, iii) a paradox where more confident students tend to be less interested, and vice-versa. Pedagogical implications arising from the results are discussed with an eye on enhancing the teaching of data analytics and programming skills to this target population.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"86 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263434","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}
Pub Date : 2023-08-21DOI: 10.1080/26939169.2023.2249969
Travis Weiland, Immanuel Williams
{"title":"Culturally Relevant Data in Teaching of Statistics and Data Science Courses","authors":"Travis Weiland, Immanuel Williams","doi":"10.1080/26939169.2023.2249969","DOIUrl":"https://doi.org/10.1080/26939169.2023.2249969","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43650351","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}
Pub Date : 2023-08-21DOI: 10.1080/26939169.2023.2249956
Brenna Curley, J. Downey
{"title":"Implementation of Alternative Grading Methods in a Mathematical Statistics Course","authors":"Brenna Curley, J. Downey","doi":"10.1080/26939169.2023.2249956","DOIUrl":"https://doi.org/10.1080/26939169.2023.2249956","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47668872","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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