The purpose of this study is to explore the expressions of confidence by a group of South African mathematics teachers about teaching mathematics and statistics concepts from various perspectives. The participants were 75 mathematics teachers who were teaching Grades 4 to 12 in KwaZulu-Natal (KZN) schools. They then were asked to express their opinion on their level of confidence in teaching using 17 confidence items on a 5 point Likert scale, graded from very low to very high. The study drew upon factor analysis, Rasch analysis as well as regression analysis. The findings suggest that teachers’ confidence in teaching mathematics concepts is quite different from their confidence in teaching statistics concepts and those which require connections across topics. Furthermore, the study has also found differences in teachers’ confidence level by gender during the middle teaching years as well as a significant interaction between phases of teaching and whether or not teachers completed additional professional qualifications.
{"title":"ANALYSIS OF TEACHERS’ CONFIDENCE IN TEACHING MATHEMATICS AND STATISTICS","authors":"Odette Umugiraneza, S. Bansilal, D. North","doi":"10.52041/serj.v21i3.422","DOIUrl":"https://doi.org/10.52041/serj.v21i3.422","url":null,"abstract":"The purpose of this study is to explore the expressions of confidence by a group of South African mathematics teachers about teaching mathematics and statistics concepts from various perspectives. The participants were 75 mathematics teachers who were teaching Grades 4 to 12 in KwaZulu-Natal (KZN) schools. They then were asked to express their opinion on their level of confidence in teaching using 17 confidence items on a 5 point Likert scale, graded from very low to very high. The study drew upon factor analysis, Rasch analysis as well as regression analysis. The findings suggest that teachers’ confidence in teaching mathematics concepts is quite different from their confidence in teaching statistics concepts and those which require connections across topics. Furthermore, the study has also found differences in teachers’ confidence level by gender during the middle teaching years as well as a significant interaction between phases of teaching and whether or not teachers completed additional professional qualifications. ","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44406407","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 is the editorial introducing SERJ Volume 21, Issue 3.
这是介绍SERJ第21卷第3期的社论。
{"title":"Editorial and Front Matter Issue 3 2022","authors":"Jennifer J. Kaplan","doi":"10.52041/serj.v21i3.639","DOIUrl":"https://doi.org/10.52041/serj.v21i3.639","url":null,"abstract":"This is the editorial introducing SERJ Volume 21, Issue 3.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45330522","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}
We examined students’ initial and concluding attitudes toward statistics based on course delivery methods. Students enrolled in either traditional or online undergraduate statistics courses (N = 196) completed the Survey of Attitudes Toward Statistics-36. At the beginning of the semester, students in traditional courses felt better about the course and believed it would be easier, compared to students taking statistics online. Attitude differences, however, were mitigated as traditional courses were forced online by the pandemic, and distinct attitudinal differences were not observed at the semester’s end. With limited offerings and restrictions on the delivery of traditional courses in the COVID-19 era, statistics educators should be cognizant of student attitudes, their potential for change, and how to best influence positive attitude shifts for different instructional formats.
{"title":"INITIAL ATTITUDES TOWARD STATISTICS ARE BETTER IN TRADITIONAL COMPARED TO ONLINE COURSES, AT LEAST UNTIL COVID-19","authors":"Hiroki Matsuo, Aleise L. Nooner, A. R. Pearce","doi":"10.52041/serj.v21i3.90","DOIUrl":"https://doi.org/10.52041/serj.v21i3.90","url":null,"abstract":"We examined students’ initial and concluding attitudes toward statistics based on course delivery methods. Students enrolled in either traditional or online undergraduate statistics courses (N = 196) completed the Survey of Attitudes Toward Statistics-36. At the beginning of the semester, students in traditional courses felt better about the course and believed it would be easier, compared to students taking statistics online. Attitude differences, however, were mitigated as traditional courses were forced online by the pandemic, and distinct attitudinal differences were not observed at the semester’s end. With limited offerings and restrictions on the delivery of traditional courses in the COVID-19 era, statistics educators should be cognizant of student attitudes, their potential for change, and how to best influence positive attitude shifts for different instructional formats.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45608768","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}
Eric A. Vance, David R. Glimp, Nathan D. Pieplow, Jane M. Garrity, B. Melbourne
Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.
{"title":"INTEGRATING THE HUMANITIES INTO DATA SCIENCE EDUCATION","authors":"Eric A. Vance, David R. Glimp, Nathan D. Pieplow, Jane M. Garrity, B. Melbourne","doi":"10.52041/serj.v21i2.42","DOIUrl":"https://doi.org/10.52041/serj.v21i2.42","url":null,"abstract":"Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44965741","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}
With the advent of data science, recommendations for teaching statistical modelling include adopting a greater focus on prediction. However, there has been minimal research about the design of tasks for teaching predictive modelling from a data science. Therefore, a design-based research approach was used to develop a new web-based task that explored: accessing and using dynamic movie ratings data from an API; developing a model to generate prediction intervals; and modifying and running provided R code in the browser. The task was implemented within a face-to-face teaching experiment involving six high school statistics teachers. Analysis of the teacher responses to the task identified four key task design features that appeared to stimulate development of statistical and computational ideas related to predictive modelling and APIs.
{"title":"INTRODUCING HIGH SCHOOL STATISTICS TEACHERS TO PREDICTIVE MODELLING BY EXPLORING DYNAMIC MOVIE RATINGS DATA: A FOCUS ON TASK DESIGN","authors":"Anna-Marie Fergusson, M. Pfannkuch","doi":"10.52041/serj.v21i2.49","DOIUrl":"https://doi.org/10.52041/serj.v21i2.49","url":null,"abstract":"With the advent of data science, recommendations for teaching statistical modelling include adopting a greater focus on prediction. However, there has been minimal research about the design of tasks for teaching predictive modelling from a data science. Therefore, a design-based research approach was used to develop a new web-based task that explored: accessing and using dynamic movie ratings data from an API; developing a model to generate prediction intervals; and modifying and running provided R code in the browser. The task was implemented within a face-to-face teaching experiment involving six high school statistics teachers. Analysis of the teacher responses to the task identified four key task design features that appeared to stimulate development of statistical and computational ideas related to predictive modelling and APIs.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44710171","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}
Rolf Biehler, R.D. De Veaux, J. Engel, S. Kazak, Daniel Frischemeier
A very warm welcome to this Special Issue of the Statistics Education Research Journal (SERJ) on data science education. Our hope is to give an overview of selected theoretical thoughts and empirical studies on data science education from a statistics education research perspective. Data science education is rapidly developing but research into data science education is still in its infancy. The current issue presents a snapshot of this developing field.
{"title":"EDITORIAL: RESEARCH ON DATA SCIENCE EDUCATION","authors":"Rolf Biehler, R.D. De Veaux, J. Engel, S. Kazak, Daniel Frischemeier","doi":"10.52041/serj.v21i2.606","DOIUrl":"https://doi.org/10.52041/serj.v21i2.606","url":null,"abstract":"A very warm welcome to this Special Issue of the Statistics Education Research Journal (SERJ) on data science education. Our hope is to give an overview of selected theoretical thoughts and empirical studies on data science education from a statistics education research perspective. Data science education is rapidly developing but research into data science education is still in its infancy. The current issue presents a snapshot of this developing field.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70657561","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}
Holistic data science education places data science in the context of real world applications, emphasizing the purpose for which data were collected, the pedigree of the data, the meaning inherent in the daa, the deploying of sustainable solutions, and the communication of key findings for addressing the original problem. As such it spends less emphasis on coding, computing, and high-end black-box algorithms. We argue that data science education must move toward a holistic curriculum, and we provide examples and reasons for this emphasis.
{"title":"TOWARD HOLISTIC DATA SCIENCE EDUCATION","authors":"R.D. De Veaux, R. Hoerl, R. Snee, P. Velleman","doi":"10.52041/serj.v21i2.40","DOIUrl":"https://doi.org/10.52041/serj.v21i2.40","url":null,"abstract":"Holistic data science education places data science in the context of real world applications, emphasizing the purpose for which data were collected, the pedigree of the data, the meaning inherent in the daa, the deploying of sustainable solutions, and the communication of key findings for addressing the original problem. As such it spends less emphasis on coding, computing, and high-end black-box algorithms. We argue that data science education must move toward a holistic curriculum, and we provide examples and reasons for this emphasis.\u0000 ","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46222347","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}
Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.
{"title":"A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING","authors":"Susanne Podworny, Sven Hüsing, Carsten Schulte","doi":"10.52041/serj.v21i2.46","DOIUrl":"https://doi.org/10.52041/serj.v21i2.46","url":null,"abstract":"Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41743799","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}
Mine Çetinkaya-Rundel, M. Dogucu, Wendy Rummerfield
Many data science applications involve generating questions, acquiring data and preparing it for analysis—be it exploratory, inferential, or modeling focused—and communicating findings. Most data science curricula address each of these steps as separate units in a course or as separate courses. Open-ended term projects, on the other hand, allow students to put each of these steps into practice, sequentially and iteratively. In this paper we discuss what we mean by data science projects, why they are crucial in introductory data science courses, who works on these projects and how, when in the term they can be implemented, and where they can be shared.
{"title":"5Ws AND 1H OF TERM PROJECTS IN THE INTRODUCTORY DATA SCIENCE CLASSROOM","authors":"Mine Çetinkaya-Rundel, M. Dogucu, Wendy Rummerfield","doi":"10.52041/serj.v21i2.37","DOIUrl":"https://doi.org/10.52041/serj.v21i2.37","url":null,"abstract":"Many data science applications involve generating questions, acquiring data and preparing it for analysis—be it exploratory, inferential, or modeling focused—and communicating findings. Most data science curricula address each of these steps as separate units in a course or as separate courses. Open-ended term projects, on the other hand, allow students to put each of these steps into practice, sequentially and iteratively. In this paper we discuss what we mean by data science projects, why they are crucial in introductory data science courses, who works on these projects and how, when in the term they can be implemented, and where they can be shared.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47055289","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}
Data science is a new field of research, with growing interest in recent years, that focuses on extracting knowledge and value from data. New data science education programs, which are being launched at a growing rate, are designed for multiple levels, beginning with elementary school pupils. Machine learning is an important element of data science that requires an extensive background in mathematics. While it is possible to teach the principles of machine learning as a black box, it might be difficult to improve algorithm performance without a white box understanding of the underlaying learning algorithms. In this paper, we suggest pedagogical methods to support white box understanding of machine learning algorithms for learners who lack the needed graduate level of mathematics, particularly high school computer science pupils.
{"title":"MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH","authors":"Koby Mike, O. Hazzan","doi":"10.52041/serj.v21i2.45","DOIUrl":"https://doi.org/10.52041/serj.v21i2.45","url":null,"abstract":"Data science is a new field of research, with growing interest in recent years, that focuses on extracting knowledge and value from data. New data science education programs, which are being launched at a growing rate, are designed for multiple levels, beginning with elementary school pupils. Machine learning is an important element of data science that requires an extensive background in mathematics. While it is possible to teach the principles of machine learning as a black box, it might be difficult to improve algorithm performance without a white box understanding of the underlaying learning algorithms. In this paper, we suggest pedagogical methods to support white box understanding of machine learning algorithms for learners who lack the needed graduate level of mathematics, particularly high school computer science pupils.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49097428","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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